DGE Analyses
Amanda D. Clark
Purpose
WORDS
### Sources & Resources Sources and resources are linked where applicable ## Setting Up ### Setting Up Environment
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library(cowplot)
# directory for input files
indir <- "../R_outputs/QuantPrep_Filter"
# make a directory for output files
if (! dir.exists("../R_outputs/DGE_Analyses")) {
dir.create("../R_outputs/DGE_Analyses")
}
outdir <- "../R_outputs/DGE_Analyses"Setting up Input Files
words
I can individual load the data for each pipeline separately, and add them to a list
# reading in count data
hf_htsh <- read.csv(file.path(indir, "hard_filtered_htsh.csv"), header = T, row.names = 1 ) #Hard-filtered (see Purpose) count matrices
hf_htss <- read.csv(file.path(indir,"hard_filtered_htss.csv"), header = T, row.names = 1 )
hf_kall <- read.csv(file.path(indir,"hard_filtered_kallisto.csv"), header = T, row.names = 1 )
hf_salm <- read.csv(file.path(indir,"hard_filtered_salmon.csv"), header = T, row.names = 1 )
hf_strh <- read.csv(file.path(indir,"hard_filtered_strgtieh.csv"), header = T, row.names = 1 )
hf_strs <- read.csv(file.path(indir,"hard_filtered_strgties.csv"), header = T, row.names = 1)
sf_htsh <- read.csv(file.path(indir,"soft_filtered_htsh.csv"), header = T, row.names = 1) #Soft-filtered count matrices
sf_htss <- read.csv(file.path(indir,"soft_filtered_htss.csv"), header = T, row.names = 1)
sf_kall <- read.csv(file.path(indir,"soft_filtered_kallisto.csv"), header = T, row.names = 1)
sf_salm <- read.csv(file.path(indir,"soft_filtered_salmon.csv"), header = T, row.names = 1)
sf_strh <- read.csv(file.path(indir,"soft_filtered_strgtieh.csv"), header = T, row.names = 1)
sf_strs <- read.csv(file.path(indir,"soft_filtered_strgties.csv"), header = T, row.names = 1)
# make list of dataframes
datlist <- list(hf_htsh=hf_htsh,hf_htss=hf_htss,hf_kall=hf_kall,hf_salm=hf_salm,
hf_strh=hf_strh,hf_strs=hf_strs,sf_htsh=sf_htsh,sf_htss=sf_htss,
sf_kall=sf_kall,sf_salm=sf_salm,sf_strh=sf_strh,sf_strs=sf_strs)
# adding in sample metadata
samples <- read.table("../R_inputs/samples.txt", header = T) #Read in sample table
#make new column for treatment (control vs Experiment)
samples <- samples %>% dplyr::select(SRRID, SAMPNAME) %>% #select the sample ID and name
mutate(Treat = ifelse(grepl("C", samples$SAMPNAME), "Restricted", #make new column for treatment (restricted vs adlib)
ifelse(grepl("E", samples$SAMPNAME), "AdLib", "Error")))
## make a note to change
head(samples)
Or I can generate a List object with the file names and data
# sample metadata
samples <- read.table("../R_inputs/samples.txt", header = T) #Read in sample table
#make new column for treatment (control vs Experiment)
samples <- samples %>% dplyr::select(SRRID, SAMPNAME) %>% #select the sample ID and name
mutate(Treat = ifelse(grepl("C", samples$SAMPNAME), "Restricted", #make new column for treatment (restricted vs adlib)
ifelse(grepl("E", samples$SAMPNAME), "AdLib", "Error")))
# generate data vector
files <- list() # empty list for file paths
count_data <- vector(mode = "list", length = 2) # empty list for data vector
files <- list.files(indir, ".csv", full.names = T) # populate list from input directory
count_data <- list(f_name = c(file_path_sans_ext(basename(files))), f_content = files %>% map(read.csv, header = T, row.names =1)) # populate list with file names, paths, and content
names(count_data$f_content) <- count_data$f_name # name matrices based on file namesDGE Functions
Library Visualization Function
words https://multithreaded.stitchfix.com/blog/2015/10/15/multiple-hypothesis-testing/ # explains MHT well, and in depth. pull what is needed, link the rest
run.Vis <- function(x, y) {
y <- y[[1]][1]
cat("Currently visualizing libraries for pipeline:", y, "\n\n") #print which file is being processed
colnames(x) <- c(samples$SAMPNAME) #add column names
cat("\nColumn names:", names(x), "\n\n") #print column names
# possible issue, this object may not be the same across programs, but that shouldn't be a problem if it's just a data format. all information going into downstream programs are the same.
dat <- DGEList(x, group = as.factor(c(samples$Treat))) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples
print(dat)
# look at raw lib size in parallel
barplot(dat$samples$lib.size*1e-6, names = 1:10, ylab = "Library size (millions)", xlab = y) #Make barplot of the library sizes
# Saved in the object
b.plot = recordPlot()
dev.off()
cpm <- cpm(dat)
lcpm <- cpm(dat, log = TRUE)
L <- mean(dat$samples$lib.size) * 1e-6
M <- median(dat$samples$lib.size) * 1e-6
cat("\n", "Mean and Median Library Size in Millions:", c(L, M),"\n")
cnttab <- table(rowSums(dat$counts == 0) == 10)
cnttab
# MDS Plots
col.group <- as.factor(c(samples$Treat))
levels(col.group) <- brewer.pal(nlevels(col.group), "Set1")
col.group <- as.character(col.group)
mds.plot <- plotMDS(lcpm, labels = samples$SAMPNAME, col = col.group, main = paste0("Pipeline: ", y))
# Saved in the object
mds.plot = recordPlot()
dev.off()
# Density Plots to observe filtering effects (may need to reformat output to a list and then organize them after running the function to compare hard/soft filters)
col.group <- brewer.pal(ncol(x), "Set3")
lcpm.cutoff <- log2(10/M + 2/L)
plot(density(lcpm[,1]), col = col.group[1], lwd = 2, ylim = c(0,0.26), las = 2, main = y)
for (i in 2:ncol(x)){
den <- density(lcpm[,i])
lines(den$x, den$y, col = col.group[i], lwd = 2)
}
legend("topright", samples$SAMPNAME, text.col = col.group, bty = "n")
abline(v = lcpm.cutoff, lty = 3)
# Saved in the object
density.res = recordPlot()
dev.off()
# Box Plots
dat2 <- calcNormFactors(dat, method = "TMM")
lcpm2 <- cpm(dat2, log=TRUE)
print(head(lcpm))
print(head(lcpm2))
dat$samples$norm.factors
dat2$samples$norm.factors
par(mfrow=c(1,2)) #getting
boxplot(lcpm, las = 2, col = col.group, main = "Unnormalized data", ylab = "Log-CPM")
boxplot(lcpm2, las = 2, col = col.group, main = "Normalized data", ylab = "Log-CPM")
box.res = recordPlot()
dev.off()
pdf(file = paste0(outdir,"/",y,"_dataExploration.pdf"))
print(b.plot)
print(mds.plot)
print(density.res)
print(box.res)
dev.off()
}DESeq2 DGE Function
words
run.DESeq <- function(x, y) {
y <- y[[1]][1]
cat("Currently proccessing:", y, "with DESeq \n") #print which file is being processed
colnames(x) <- c(samples$SAMPNAME) #add column names
cat("\nColumn names:", names(x), "\n\n") #print column names
dat <- DESeqDataSetFromMatrix(countData = x, colData = samples,
design = ~Treat) #create DESeq object, merging counts, metadata, and specifies the predictor variable for gene counts
print(dat)
cat("\nResults Below: \n")
mod <- DESeq(dat, minReplicatesForReplace = Inf) #running the DESeq function. minReplicatesForReplace=Inf prevents replacement of outlier counts
res <- results(mod, independentFiltering = FALSE,cooksCutoff = FALSE, contrast = c("Treat", "Restricted", "AdLib"),
pAdjustMethod = "fdr") #store results table. skipping outlier adjustments and additional low count filtering. using a false discovery rate p-value adjustment
print(head(res))
print(summary(res))
# make data frame output, reorder, and filter
reslist <- list( GeneID = res@rownames, meanExpr = res@listData$baseMean, logFC = res@listData$log2FoldChange,
pval = res@listData$pvalue, adj.pval = res@listData$padj)
resdf <- as.data.frame(do.call(cbind, reslist)) %>% mutate(meanExpr = as.numeric(meanExpr), pval = as.numeric(pval),
adj.pval = as.numeric(adj.pval))
resOrdered <- resdf[order(as.numeric(resdf$adj.pval)),] #results reordered by the adjusted pvalue
resSig <- subset(resOrdered, as.numeric(adj.pval) < 0.05)
print(head (resSig))
print(summary(resSig))
out <- resSig
write.csv(as.data.frame(out),file=paste0(outdir,"/",y,"_DESeq2.csv")) #write results to a new csv
pdf(file = paste0(outdir,"/",y,"_DESeq.pdf"))
DESeq2::plotMA(res)
dev.off()
}edgeR DGE Function
words
run.EdgeR <- function(x, y) {
y <- y[[1]][1]
cat("Currently proccessing:", y, "with edgeR \n") #print which file is being processed
colnames(x) <- c(samples$SAMPNAME) #add column names
cat("\nColumn names:", names(x), "\n\n") #print column names
dat <- DGEList(x, group = samples$Treat) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples
# est common & tagwise dispersion
mod <- estimateCommonDisp(dat)
mod <- estimateTagwiseDisp(mod)
# perform exact test btwn caloric restriction & ad lib groups, store as 'res'
modTest <- exactTest(mod)
res <- topTags(modTest, n = nrow(modTest$table))
# extract significant differentially expressed genes, sort, & write to csv
resOrdered <- res$table[order(res$table$logFC),]
resSig <- resOrdered[resOrdered$FDR<0.05,]
print(head(resOrdered))
out <- resSig %>% dplyr::select(logFC, logCPM, PValue, FDR) %>% dplyr::rename(meanExpr = logCPM, pval = PValue, adj.pval = FDR)
write.csv(as.data.frame(out),file = paste0(outdir,"/",y,"_edgeR.csv")) #write results to a new csv
cat("The number of significant DE genes is: ", nrow(resSig),"\n\n")
pdf(file = paste0(outdir,"/",y,"_edgeR.pdf"))
edgeR::plotMD.DGEExact(modTest)
#plotMD(res, column = 5, main = paste(colnames(res)[1],y,sep = "_"), xlim = c(-0.1,20))
dev.off()
}LimmaVoom DGE Function
words Summarize & simplify: “What is voom doing? Counts are transformed to log2 counts per million reads (CPM), where “per million reads” is defined based on the normalization factors we calculated earlier A linear model is fitted to the log2 CPM for each gene, and the residuals are calculated A smoothed curve is fitted to the sqrt(residual standard deviation) by average expression (see red line in plot above) The smoothed curve is used to obtain weights for each gene and sample that are passed into limma along with the log2 CPMs. More details at https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29”
run.LimVoo <- function(x,y) {
y <- y[[1]][1]
cat("Currently proccessing:", y ,"with Limma-Voom \n") #print which file is being processed by which function as a sanity check
colnames(x) <- c(samples$SAMPNAME) #add column names to the data object
cat("\nColumn names:", names(x), "\n\n") #print column names as a sanity check for order
Treat <- c(samples$Treat)
group=Treat
dat <- DGEList(x, group = Treat) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples
#print(class(dat))
# Normalization (based on the plots, is this necessary?)
dat <- calcNormFactors(dat, method = "TMM")
dat$samples$norm.factors
dat
mod <- model.matrix(~0 + group)
mod
varMod <- voom(dat, mod, plot = T) # Would be nice to stop and compare hard/soft filtering again here
modFit <- lmFit(varMod, mod)
#print(head(coef(modFit)))
contr <- makeContrasts(groupAdLib - groupRestricted, levels = colnames(coef(modFit)))
#print(head(contr))
fitContr <- contrasts.fit(modFit, contr)
fitContr <- eBayes(fitContr)
res <- topTable(fitContr, sort.by = "P", n = Inf)
print(head(res, 8))
cat("Results where FDR is less than 0.01: ", length(which(res$adj.P.Val < 0.01)), "\n")
cat("Results where FDR is less than 0.05: ", length(which(res$adj.P.Val < 0.05)), "\n")
cat("Results where FDR is less than 0.1: ", length(which(res$adj.P.Val < 0.1)), "\n")
out <- res %>% dplyr::select(logFC, AveExpr, P.Value, adj.P.Val) %>% dplyr::rename(meanExpr = AveExpr, pval = P.Value, adj.pval = adj.P.Val)
print(head(out))
etRes <- decideTests(fitContr)
print(summary(etRes))
write.csv(as.data.frame(out),file = paste0(outdir,"/",y,"_LimmaVoom.csv")) #write results to a new csv
pdf(file = paste0(outdir,"/",y,"_LimmaVoom.pdf"))
plotMD(fitContr, column = 1, status = etRes[,1], main = paste(colnames(fitContr)[1],y,sep = "_"), xlim = c(-0.1,20))
varMod <- voom(dat, mod, plot = T)
dev.off()
}Ballgown DGE
words Summarize & simplify: There are many ballgown specific input files that make it difficult to use the previously filtered data with these programs. Only things processed with stringtie with the for ballgown output are readily formatted for this program. These will not be run within a function.? Some resources: https://rnabio.org/module-03-expression/0003/04/01/DE_Visualization/ https://rstudio-pubs-static.s3.amazonaws.com/289617_cb95459057764fdfb4c42b53c69c6d3f.html https://davetang.org/muse/2017/10/25/getting-started-hisat-stringtie-ballgown/
# We loaded our "phenotype" data in the beginning, so we don't need to repeat this step.
# create a ballgown object for the star and hisat2 outputs; stringtie and ballgown are complementary programs
bg_star <- ballgown(dataDir = "../R_inputs/ballgown_star/", samplePattern = "SRR", pData = samples)
bg_hisat <- ballgown(dataDir = "../R_inputs/ballgown_hisat/", samplePattern = "SRR", pData = samples)
# check out the objects
class(bg_star)
class(bg_hisat)
bg_star
bg_hisat
# filtering, following previous logic for pipeline specific
bg_star_f1 <- ballgown::subset(bg_star,
"rowSums(gexpr(bg_star)==0) <= 5",
genomesubset=TRUE) # first filter, remove rows with 6 or more 0s
bg_star_f1
bg_star_fltrd <- ballgown::subset(bg_star_f1,
"rowSums(gexpr(bg_star_f1)) >= 21") # second filter, remove rows that sum to less than 21
bg_star_fltrd
bg_hisat_f1 <- ballgown::subset(bg_hisat,
"rowSums(gexpr(bg_hisat)==0) <= 5",
genomesubset=TRUE) # first filter, remove rows with 6 or more 0s
bg_hisat_f1
bg_hisat_fltrd <- ballgown::subset(bg_hisat_f1,
"rowSums(gexpr(bg_hisat_f1)) >= 21") # second filter, remove rows that sum to less than 21
bg_hisat_fltrd
# run dge analysis and output data to file (should filter by qvalue? -- what did I filter by with other tables?)
bg_star_genes <- stattest(bg_star_fltrd,
feature="gene",
covariate="Treat",
getFC=TRUE, meas="FPKM")
dim(bg_star_genes)
table(bg_star_genes$qval<0.05)
bg_hisat_genes <- stattest(bg_hisat_fltrd,
feature="gene",
covariate="Treat",
getFC=TRUE, meas="FPKM")
dim(bg_hisat_genes)
table(bg_hisat_genes$qval<0.05)
# output results
# extract significant differentially expressed genes, sort, & write to csv
bg_hisat_genes[,"de"] <- log2(bg_hisat_genes[,"fc"])
sigpi = which(bg_hisat_genes[,"pval"]<0.05)
sigp = bg_hisat_genes[sigpi,]
sigde = which(abs(sigp[,"de"]) >= 2)
sig_tn_de = sigp[sigde,]
o = order(sig_tn_de[,"qval"], -abs(sig_tn_de[,"de"]), decreasing=FALSE)
output = sig_tn_de[o,c("id","fc","pval","qval","de")]
write.csv(as.data.frame(output),file = paste0(outdir,"/","hisat_Ballgown.csv")) #write results to a new csv
bg_star_genes[,"de"] <- log2(bg_star_genes[,"fc"])
sigpi = which(bg_hisat_genes[,"pval"]<0.05)
sigp = bg_hisat_genes[sigpi,]
sigde = which(abs(sigp[,"de"]) >= 2)
sig_tn_de = sigp[sigde,]
o = order(sig_tn_de[,"qval"], -abs(sig_tn_de[,"de"]), decreasing=FALSE)
output <- sig_tn_de[o,c("id","fc","pval","qval","de")]
write.csv(as.data.frame(output),file = paste0(outdir,"/","star_Ballgown.csv")) #write results to a new csv
# visualize results
bg_star_genes$mean <- rowMeans(texpr(bg_star_fltrd))
bg_star_plot <- ggplot(bg_star_genes, aes(log2(mean), log2(fc), colour = qval<0.05)) +
scale_color_manual(values=c("#999999", "#FF0000")) +
geom_point() +
geom_hline(yintercept=0)
bg_hisat_genes$mean <- rowMeans(texpr(bg_hisat_fltrd))
bg_hisat_plot <- ggplot(bg_hisat_genes, aes(log2(mean), log2(fc), colour = qval<0.05)) +
scale_color_manual(values=c("#999999", "#FF0000")) +
geom_point() +
geom_hline(yintercept=0)
bg_star_plot
bg_hisat_plot
Apply DGE functions
I am applying each function in loop here
cnt <- 1
for (i in datlist){
run.Vis(i, names(datlist)[cnt])
cnt <- cnt +1
}
cnt <- 1
for (i in datlist){
run.DESeq(i, names(datlist)[cnt])
cnt <- cnt +1
}
cnt <- 1
for (i in datlist){
run.EdgeR(i, names(datlist)[cnt])
cnt <- cnt +1
}
cnt <- 1
for (i in datlist){
run.LimVoo(i, names(datlist)[cnt])
cnt <- cnt +1
}I can also map or mapply over the data set, looping over each DEseq function
# List of functions needed to run on count matrices
funct <- c( "run.Vis","run.DESeq", "run.EdgeR", "run.LimVoo")
# Apply each function to each count data set in List object
for (func in funct) {
mapply(func, count_data$f_content, count_data$f_name)
}Currently visualizing libraries for pipeline: hard_filtered_htsh
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 269 325 338 388 341 271 351 249 264 441
FUN_000005 1114 1569 1092 1321 2181 2014 2129 2293 2210 2940
FUN_000006 194 273 224 251 468 361 332 474 424 579
FUN_000007 2240 3254 1947 2347 2492 1713 2822 2181 2464 3268
FUN_000008 99 364 241 222 179 150 107 223 229 231
14296 more rows ...
$samples
Mean and Median Library Size in Millions: 21.78049 20.67801 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 4.176416 4.048445 4.3674991 4.647334 3.5752138 3.659400 4.2648155 3.175810 3.611003 3.7621706
FUN_000005 6.220919 6.313417 6.0549387 6.411098 6.2429526 6.543996 6.8596819 6.365701 6.666861 6.4908132
FUN_000006 3.707696 3.798431 3.7772371 4.021848 4.0289313 4.070490 4.1849221 4.097576 4.290432 4.1526264
FUN_000007 7.227775 7.364923 6.8883434 7.239605 6.4350486 6.310707 7.2659438 6.293537 6.823682 6.6432572
FUN_000008 2.746837 4.211086 3.8820898 3.845785 2.6554257 2.814502 2.5665850 3.018416 3.407469 2.8381336
FUN_000009 -1.773739 -1.009086 -0.9506003 -1.811569 -0.9769934 -0.112509 -0.9217186 -1.881279 -1.122484 -0.3881246
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 4.288257 4.050506 4.4003803 4.594051 3.5792943 3.6276310 4.2777725 3.214995 3.527944 3.7271295
FUN_000005 6.333191 6.315506 6.0879330 6.357686 6.2470880 6.5120522 6.8727077 6.405278 6.583263 6.4555925
FUN_000006 3.819318 3.800484 3.8100353 3.968664 4.0330295 4.0386704 4.1978743 4.136968 4.207143 4.1175351
FUN_000007 7.340117 7.367016 6.9213601 7.186169 6.4391853 6.2787680 7.2789730 6.333111 6.740077 6.6080333
FUN_000008 2.857712 4.213150 3.9149053 3.792638 2.6594481 2.7828948 2.5793567 3.057550 3.324503 2.8032822
FUN_000009 -1.696979 -1.008177 -0.9241194 -1.849017 -0.9743778 -0.1417137 -0.9116873 -1.856377 -1.189943 -0.4196272
Currently visualizing libraries for pipeline: hard_filtered_htss
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 256 316 329 360 344 270 330 239 270 418
FUN_000005 1122 1597 1124 1299 2272 2140 2197 2417 2220 2928
FUN_000006 174 240 203 218 433 351 323 426 388 501
FUN_000007 2180 3166 1952 2262 2491 1795 2900 2149 2446 3121
FUN_000008 120 400 249 259 209 182 122 249 268 242
14296 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 23.2617 21.99226 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 4.019083 3.950052 4.248080 4.487181 3.484842 3.508325 4.047829 3.004274 3.537596 3.620980
FUN_000005 6.145023 6.280968 6.015938 6.334510 6.198892 6.485347 6.776438 6.328410 6.567711 6.420653
FUN_000006 3.465628 3.555701 3.555518 3.767110 3.814522 3.884319 4.017059 3.831314 4.057441 3.880617
FUN_000007 7.102428 7.267474 6.811436 7.134053 6.331506 6.231986 7.176682 6.159051 6.707455 6.512656
FUN_000008 2.934619 4.288441 3.848234 4.014291 2.773076 2.944565 2.624974 3.062788 3.526949 2.839798
FUN_000009 1.273124 1.741883 2.117911 1.929611 2.109904 2.215705 1.838380 2.127757 2.527024 1.857021
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 4.122509 3.942661 4.278011 4.414241 3.489479 3.485646 4.062345 3.052664 3.461097 3.599394
FUN_000005 6.248867 6.273554 6.045971 6.261368 6.203573 6.462531 6.791028 6.377286 6.490713 6.398949
FUN_000006 3.568799 3.548319 3.585360 3.694350 3.819169 3.861604 4.031574 3.879940 3.980770 3.859008
FUN_000007 7.206332 7.260057 6.841487 7.060878 6.336188 6.209174 7.191275 6.207919 6.630451 6.490951
FUN_000008 3.037435 4.281044 3.878119 3.941459 2.777678 2.921961 2.639344 3.111199 3.450455 2.818311
FUN_000009 1.373445 1.734596 2.147353 1.858032 2.114456 2.193253 1.852581 2.175696 2.451102 1.835763
Currently visualizing libraries for pipeline: hard_filtered_kallisto
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 149 175 181 208 190 141 189 136 148 240
FUN_000005 637 920 634 766 1255 1151 1212 1312 1237 1681
FUN_000006 107 153 120 141 259 190 174 250 223 319
FUN_000007 1271 1883 1197 1335 1403 993 1593 1247 1406 1846
FUN_000008 70 230 143 149 112 95 70 134 137 143
14296 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 13.64146 12.93434 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 4.000170 3.820597 4.152520 4.420829 3.425608 3.420350 4.052482 2.991095 3.452382 3.563222
FUN_000005 6.085989 6.202702 5.952490 6.294379 6.132422 6.432029 6.722613 6.237140 6.498446 6.356014
FUN_000006 3.527648 3.628924 3.565598 3.864626 3.867307 3.845550 3.934281 3.857234 4.037315 3.969306
FUN_000007 7.081034 7.234559 6.867751 7.094653 6.292931 6.219397 7.116491 6.163979 6.682916 6.490866
FUN_000008 2.925126 4.211291 3.815703 3.943463 2.676751 2.860189 2.641032 2.970119 3.342511 2.828290
FUN_000009 1.044054 1.253268 2.015929 1.488350 1.804675 1.844596 1.268461 1.354294 2.106342 1.366631
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 4.093236 3.814769 4.165635 4.340900 3.435392 3.400927 4.063512 3.048664 3.387398 3.548506
FUN_000005 6.179696 6.196829 5.965686 6.214041 6.142327 6.412376 6.733731 6.295666 6.432676 6.341146
FUN_000006 3.620388 3.623104 3.578656 3.784961 3.877128 3.826060 3.945303 3.915286 3.972033 3.954546
FUN_000007 7.174839 7.228682 6.880962 7.014250 6.302839 6.199748 7.127612 6.222500 6.617133 6.475995
FUN_000008 3.017263 4.205450 3.828788 3.863754 2.686438 2.840890 2.651891 3.027672 3.277598 2.813692
FUN_000009 1.131439 1.247710 2.028659 1.412144 1.814162 1.825693 1.278882 1.409601 2.042736 1.352552
Currently visualizing libraries for pipeline: hard_filtered_salmon
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 127 153 158 193 168 134 170 121 126 199
FUN_000005 495 730 487 606 980 884 945 1026 980 1322
FUN_000006 71 116 87 97 178 133 128 192 170 230
FUN_000007 1215 1786 1108 1265 1335 944 1545 1192 1360 1775
FUN_000008 54 208 129 132 100 83 67 123 110 134
14296 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 11.82035 11.17817 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 3.981622 3.858952 4.209716 4.545035 3.453274 3.540352 4.092312 3.019626 3.421466 3.493106
FUN_000005 5.932688 6.099958 5.824774 6.188608 5.979009 6.244254 6.555307 6.076779 6.360832 6.206453
FUN_000006 3.154820 3.464902 3.359605 3.562803 3.535437 3.529703 3.687609 3.674551 3.847668 3.699046
FUN_000007 7.225774 7.388616 7.008322 7.248611 6.423966 6.338790 7.263519 6.292629 6.832755 6.630706
FUN_000008 2.768563 4.297549 3.920126 4.001796 2.719888 2.862137 2.770844 3.042787 3.228856 2.933048
FUN_000009 1.584730 1.746739 2.157899 2.205458 2.201107 2.285448 1.826521 1.942639 2.650457 1.718160
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 4.092552 3.840990 4.227488 4.441714 3.468429 3.514468 4.088733 3.080457 3.359803 3.500455
FUN_000005 6.044467 6.081818 5.842648 6.084750 5.994347 6.218031 6.551690 6.138709 6.298275 6.213884
FUN_000006 3.264864 3.447010 3.377253 3.460252 3.550605 3.503822 3.684045 3.735838 3.785742 3.706408
FUN_000007 7.337728 7.370448 7.026225 7.144622 6.439314 6.312563 7.259899 6.354579 6.770160 6.638140
FUN_000008 2.877982 4.279527 3.937863 3.898837 2.734897 2.836493 2.767334 3.103639 3.167340 2.940351
FUN_000009 1.690763 1.729526 2.175187 2.105342 2.215958 2.260120 1.823117 2.002085 2.589520 1.725275
Currently visualizing libraries for pipeline: hard_filtered_strgtieh
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 250 284 299 361 322 261 324 231 237 374
FUN_000005 1036 1425 1017 1209 2053 1950 2023 2160 2042 2645
FUN_000006 170 238 192 213 411 329 302 418 374 483
FUN_000007 2082 2989 1824 2164 2395 1729 2716 2085 2339 2981
FUN_000008 103 331 221 226 177 153 110 220 227 225
14296 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 20.80694 19.70391 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4
FUN_000004 4.1447179 3.9602825 4.3093162 4.6478301 3.546906331 3.6060539 4.1705685 3.11996771 3.50532705
FUN_000005 6.1897815 6.2801304 6.0704718 6.3876884 6.209468390 6.4975083 6.8065172 6.33071819 6.60151570
FUN_000006 3.5920092 3.7070740 3.6741698 3.8905233 3.896412419 3.9377359 4.0696841 3.96842405 4.15899792
FUN_000007 7.1957729 7.3478974 6.9123446 7.2268436 6.431492797 6.3241682 7.2311882 6.27979624 6.79724335
FUN_000008 2.8765800 4.1799572 3.8756837 3.9754546 2.693282396 2.8435524 2.6269823 3.05037572 3.44367016
FUN_000009 -0.2006232 -0.1310481 0.5016592 -0.1105362 -0.005665078 0.5701681 -0.3968291 -0.07072992 0.06527644
E6
FUN_000004 3.6274368
FUN_000005 6.4399278
FUN_000006 3.9938873
FUN_000007 6.6122764
FUN_000008 2.9017352
FUN_000009 0.1106819
C4 C5 C2 C3 E2 E3 C6 E1 E4
FUN_000004 4.2372617 3.9543807 4.3155487 4.568926 3.5642762 3.5910525 4.1872014 3.16642553 3.433874e+00
FUN_000005 6.2826968 6.2741997 6.0767260 6.308566 6.2269595 6.4824037 6.8232250 6.37763273 6.529511e+00
FUN_000006 3.6843242 3.7011792 3.6803853 3.811834 3.9138133 3.9227100 4.0863106 4.01510954 4.087317e+00
FUN_000007 7.2887477 7.3419629 6.9186028 7.147680 6.4489871 6.3090656 7.2478997 6.32670879 6.725229e+00
FUN_000008 2.9684331 4.1740503 3.8819054 3.896736 2.7105361 2.8286340 2.6434443 3.09680824 3.372244e+00
FUN_000009 -0.1175855 -0.1363694 0.5074921 -0.181336 0.0101593 0.5560253 -0.3822238 -0.02844179 -3.606415e-05
E6
FUN_000004 3.6193492
FUN_000005 6.4317862
FUN_000006 3.9857855
FUN_000007 6.6041338
FUN_000008 2.8936887
FUN_000009 0.1032508
Currently visualizing libraries for pipeline: hard_filtered_strgties
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 241 273 287 341 318 258 307 221 231 356
FUN_000005 1021 1409 1010 1166 2079 1994 2024 2187 2023 2599
FUN_000006 154 215 181 189 385 322 294 386 351 437
FUN_000007 2013 2923 1801 2101 2373 1753 2753 2046 2299 2887
FUN_000008 116 353 224 249 195 166 120 235 246 222
14296 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 21.88414 20.82459 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 4.031486 3.857754 4.187968 4.525181 3.451661 3.481319 3.993907 2.971615 3.387418 3.504636
FUN_000005 6.108191 6.218102 5.998013 6.294848 6.150208 6.421227 6.707769 6.263260 6.506745 6.362583
FUN_000006 3.389931 3.515641 3.527137 3.678393 3.725394 3.798660 3.931851 3.768966 3.986682 3.798237
FUN_000007 7.086613 7.269962 6.831550 7.143609 6.340800 6.235599 7.151230 6.167231 6.691081 6.514038
FUN_000008 2.985240 4.226459 3.832449 4.073669 2.753690 2.851660 2.651542 3.059228 3.477415 2.830313
FUN_000009 1.548618 1.772277 2.194649 2.113882 2.041703 2.228124 1.908808 2.239395 2.545772 2.038383
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 4.117477 3.846199 4.194365 4.435483 3.464469 3.470304 4.017632 3.022739 3.322924 3.501217
FUN_000005 6.194537 6.206483 6.004430 6.204887 6.163101 6.410127 6.731604 6.314907 6.441730 6.359135
FUN_000006 3.475662 3.504107 3.533518 3.588992 3.738219 3.787626 3.955569 3.820338 3.921988 3.794812
FUN_000007 7.173013 7.258336 6.837970 7.053599 6.353696 6.224501 7.175071 6.218874 6.626058 6.510589
FUN_000008 3.070736 4.214886 3.838838 3.984108 2.766435 2.840698 2.675064 3.110386 3.412885 2.826915
FUN_000009 1.632474 1.760977 2.200962 2.025790 2.054344 2.217245 1.932106 2.290133 2.481744 2.035024
Currently visualizing libraries for pipeline: pipeline_filtered_htsh
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 0 11 8 10 12 7 7 4 5 19
FUN_000002 1 9 8 10 1 2 7 0 0 1
FUN_000003 0 4 4 8 3 5 1 2 0 1
FUN_000004 269 325 338 388 341 271 351 249 264 441
FUN_000005 1114 1569 1092 1321 2181 2014 2129 2293 2210 2940
13973 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 21.79551 20.69244 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 -3.445959 -0.6252614 -0.7913647 -0.4446415 -0.9779168 -1.266950 -1.080076 -2.087022 -1.638125 -0.5735183
FUN_000002 -2.656539 -0.8700888 -0.7913647 -0.4446415 -2.9836518 -2.440113 -1.080076 -3.445959 -3.445959 -3.0315087
FUN_000003 -3.445959 -1.7654237 -1.5787164 -0.7222154 -2.3529189 -1.630259 -2.773737 -2.612072 -3.445959 -3.0315087
FUN_000004 4.175609 4.0474510 4.3664448 4.6461829 3.5743054 3.658382 4.263660 3.174905 3.609989 3.7611613
FUN_000005 6.220113 6.3124228 6.0538841 6.4099459 6.2420448 6.542979 6.858526 6.364797 6.665846 6.4898038
FUN_000006 3.706889 3.7974364 3.7761829 4.0206963 4.0280231 4.069472 4.183767 4.096671 4.289418 4.1516171
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 -3.450021 -0.6235803 -0.7681701 -0.4941732 -0.9763768 -1.292662 -1.067003 -2.062228 -1.698667 -0.6029724
FUN_000002 -2.610754 -0.8685826 -0.7681701 -0.4941732 -2.9858363 -2.458086 -1.067003 -3.450021 -3.450021 -3.0428676
FUN_000003 -3.450021 -1.7648809 -1.5592268 -0.7703492 -2.3533458 -1.654202 -2.769855 -2.595315 -3.450021 -3.0428676
FUN_000004 4.286576 4.0500397 4.3945857 4.5902381 3.5770258 3.626679 4.280726 3.217368 3.526848 3.7278515
FUN_000005 6.331508 6.3150408 6.0821232 6.3538641 6.2448094 6.511099 6.875676 6.407679 6.582164 6.4563230
FUN_000006 3.817640 3.8000181 3.8042519 3.9648570 4.0307577 4.037718 4.200827 4.139356 4.206046 4.1182594
Currently visualizing libraries for pipeline: pipeline_filtered_htss
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 256 316 329 360 344 270 330 239 270 418
FUN_000005 1122 1597 1124 1299 2272 2140 2197 2417 2220 2928
FUN_000006 174 240 203 218 433 351 323 426 388 501
FUN_000007 2180 3166 1952 2262 2491 1795 2900 2149 2446 3121
FUN_000008 120 400 249 259 209 182 122 249 268 242
14326 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 23.28884 22.01846 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 4.017613 3.948342 4.246172 4.485213 3.483322 3.506750 4.045855 3.002785 3.535872 3.619274
FUN_000005 6.143553 6.279258 6.014030 6.332541 6.197373 6.483773 6.774464 6.326923 6.565987 6.418946
FUN_000006 3.464157 3.553992 3.553611 3.765143 3.813002 3.882744 4.015086 3.829826 4.055717 3.878910
FUN_000007 7.100958 7.265764 6.809527 7.132084 6.329986 6.230412 7.174707 6.157564 6.705731 6.510949
FUN_000008 2.933147 4.286731 3.846327 4.012323 2.771555 2.942989 2.623003 3.061299 3.525226 2.838092
FUN_000009 1.271647 1.740174 2.116006 1.927648 2.108382 2.214129 1.836412 2.126266 2.525301 1.855315
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000004 4.148632 3.936795 4.300857 4.392875 3.481100 3.467173 4.055448 3.044699 3.467943 3.595457
FUN_000005 6.275102 6.267677 6.068899 6.239950 6.195161 6.443956 6.784116 6.369276 6.497629 6.395008
FUN_000006 3.594855 3.542458 3.608135 3.673033 3.810782 3.843104 4.024677 3.871953 3.987640 3.855070
FUN_000007 7.232583 7.254178 6.864429 7.039451 6.327775 6.190602 7.184362 6.199911 6.637368 6.487010
FUN_000008 3.063397 4.275175 3.900928 3.920122 2.769324 2.903545 2.632478 3.103232 3.457300 2.814376
FUN_000009 1.398743 1.728783 2.169810 1.837026 2.106139 2.174952 1.845751 2.167772 2.457867 1.831837
Currently visualizing libraries for pipeline: pipeline_filtered_kallisto
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 29 42 33 26 44 39 34 29 37 51
FUN_000002 26 36 33 30 10 12 23 5 0 4
FUN_000003 10 19 20 7 8 15 6 22 12 17
FUN_000004 149 175 181 208 190 141 189 136 148 240
FUN_000005 637 920 634 766 1255 1151 1212 1312 1237 1681
14429 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 13.68269 12.97388 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4
FUN_000001 1.6888730 1.8039906 1.744592 1.4832311 1.3750507 1.61252454 1.629515 0.8526073 1.50499267
FUN_000002 1.5388591 1.5916285 1.744592 1.6795896 -0.5095473 0.05946058 1.097847 -1.2098736 -2.77428056
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FUN_000006 3.5238726 3.6245683 3.560728 3.8594728 3.8633077 3.84110249 3.929088 3.8532964 4.03309717
E6
FUN_000001 1.38938191
FUN_000002 -1.55567399
FUN_000003 -0.04295002
FUN_000004 3.55901974
FUN_000005 6.35181344
FUN_000006 3.96510398
C4 C5 C2 C3 E2 E3 C6 E1 E4
FUN_000001 1.7795347 1.8000840 1.755706 1.4026705 1.3826369 1.59140942 1.6458455 0.9087818 1.4419718
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FUN_000005 6.1768774 6.1943024 5.959232 6.2042797 6.1364825 6.40546634 6.7345553 6.2941595 6.4278515
FUN_000006 3.6175546 3.6205589 3.572223 3.7752467 3.8712957 3.81917231 3.9460851 3.9137572 3.9672116
E6
FUN_000001 1.37516295
FUN_000002 -1.56449532
FUN_000003 -0.05579868
FUN_000004 3.54417302
FUN_000005 6.33681316
FUN_000006 3.95021324
Currently visualizing libraries for pipeline: pipeline_filtered_salmon
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
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13663 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 11.83682 11.19426 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
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C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.960613 1.952141 1.879967 1.504436 1.4656018 1.7474102 1.739188 1.012712 1.609685 1.514177
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FUN_000006 3.264535 3.446283 3.371925 3.453175 3.5492413 3.5019060 3.683310 3.734942 3.784720 3.705482
FUN_000007 7.337424 7.369736 7.020842 7.137468 6.4379559 6.3106453 7.259178 6.353694 6.769147 6.637225
Currently visualizing libraries for pipeline: pipeline_filtered_strgtieh
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
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FUN_000006 170 238 192 213 411 329 302 418 374 483
13576 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 20.83889 19.73533 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.734860 1.944150 1.732811 1.740387 1.5537168 1.85136374 1.7453183 1.020492 1.647022 1.605230
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FUN_000007 7.193788 7.345486 6.909872 7.224163 6.4294008 6.32199862 7.2289649 6.277748 6.795043 6.610132
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.824876 1.938910 1.737251 1.662674 1.5707049 1.8358344 1.7643075 1.064711 1.576789 1.598698
FUN_000002 1.402505 1.278322 1.765567 1.437646 -0.7240795 -0.0522592 0.9987897 -1.796484 -3.381216 -1.778488
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FUN_000005 6.280283 6.272352 6.072565 6.305026 6.2249128 6.4794000 6.8238225 6.374995 6.526880 6.431046
FUN_000006 3.681912 3.699330 3.676236 3.808302 3.9117661 3.9197110 4.0868949 4.012475 4.084689 3.985038
FUN_000007 7.286334 7.340115 6.914440 7.144139 6.4469404 6.3060621 7.2484977 6.324072 6.722598 6.603394
Currently visualizing libraries for pipeline: pipeline_filtered_strgties
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 53 64 54 42 76 77 66 51 65 91
FUN_000002 45 63 47 50 19 22 43 7 0 6
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FUN_000005 1021 1409 1010 1166 2079 1994 2024 2187 2023 2599
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13997 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 21.92688 20.86783 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.872342 1.791387 1.805609 1.540771 1.4219544 1.76168400 1.802909 0.9085955 1.587061 1.567385
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FUN_000006 3.387441 3.512630 3.523911 3.675083 3.7227769 3.79598108 3.928639 3.7664698 3.983883 3.795489
FUN_000007 7.084126 7.266950 6.828320 7.140295 6.3381845 6.23292009 7.148016 6.1647365 6.688282 6.511290
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.956602 1.780767 1.808150 1.449538 1.4349751 1.7541422 1.829495 0.959520 1.521920 1.564770
FUN_000002 1.726553 1.758655 1.613442 1.693342 -0.4258432 0.0412254 1.231050 -1.532551 -3.453562 -1.834883
FUN_000004 4.114858 3.843881 4.187313 4.427939 3.4623809 3.4709317 4.017812 3.021943 3.317966 3.499187
FUN_000005 6.191918 6.204166 5.997362 6.197328 6.1610165 6.4107770 6.731798 6.314129 6.436753 6.357108
FUN_000006 3.473043 3.501788 3.526479 3.581466 3.7361320 3.7882588 3.955749 3.819550 3.917023 3.792782
FUN_000007 7.170394 7.256019 6.830899 7.046038 6.3516108 6.2251503 7.175265 6.218096 6.621080 6.508562
Currently visualizing libraries for pipeline: soft_filtered_htsh
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 0 11 8 10 12 7 7 4 5 19
FUN_000002 1 9 8 10 1 2 7 0 0 1
FUN_000003 0 4 4 8 3 5 1 2 0 1
FUN_000004 269 325 338 388 341 271 351 249 264 441
FUN_000005 1114 1569 1092 1321 2181 2014 2129 2293 2210 2940
16526 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 21.79647 20.69348 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
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FUN_000002 -2.656603 -0.8701600 -0.7914303 -0.4447163 -2.9837136 -2.440181 -1.080134 -3.446023 -3.446023 -3.0315738
FUN_000003 -3.446023 -1.7654935 -1.5787816 -0.7222899 -2.3529793 -1.630329 -2.773798 -2.612130 -3.446023 -3.0315738
FUN_000004 4.175546 4.0473783 4.3663788 4.6461065 3.5742476 3.658310 4.263603 3.174854 3.609936 3.7610910
FUN_000005 6.220049 6.3123500 6.0538181 6.4098695 6.2419870 6.542906 6.858469 6.364746 6.665794 6.4897334
FUN_000006 3.706825 3.7973637 3.7761169 4.0206200 4.0279653 4.069400 4.183710 4.096620 4.289365 4.1515467
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 -3.449469 -0.6241997 -0.7707766 -0.4973256 -0.9745525 -1.293222 -1.066422 -2.060730 -1.698710 -0.5985501
FUN_000002 -2.611142 -0.8691663 -0.7707766 -0.4973256 -2.9848574 -2.458247 -1.066422 -3.449469 -3.449469 -3.0412093
FUN_000003 -3.449469 -1.7652684 -1.5614071 -0.7733858 -2.3519676 -1.654670 -2.769290 -2.594079 -3.449469 -3.0412093
FUN_000004 4.285007 4.0492356 4.3914117 4.5865531 3.5791174 3.625808 4.281313 3.219436 3.526559 3.7328666
FUN_000005 6.329930 6.3142307 6.0789379 6.3501676 6.2469110 6.510219 6.876263 6.409760 6.581870 6.4613644
FUN_000006 3.816074 3.7992154 3.8010861 3.9611808 4.0328525 4.036845 4.201414 4.141431 4.205755 4.1232819
Currently visualizing libraries for pipeline: soft_filtered_htss
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 0 0 2 1 4 0 2 0 0 2
FUN_000002 1 0 0 0 1 1 1 0 0 0
FUN_000003 0 4 1 3 3 5 1 2 0 0
FUN_000004 256 316 329 360 344 270 330 239 270 418
FUN_000005 1122 1597 1124 1299 2272 2140 2197 2417 2220 2928
16526 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 23.28954 22.01914 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 -3.541611 -3.541611 -2.316809 -2.757821 -2.218154 -3.541611 -2.430871 -3.541611 -3.541611 -2.793250
FUN_000002 -2.748496 -3.541611 -3.541611 -3.541611 -3.081484 -2.969675 -2.881885 -3.541611 -3.541611 -3.541611
FUN_000003 -3.541611 -1.835524 -2.802958 -1.879431 -2.452799 -1.762316 -2.881885 -2.715675 -3.541611 -3.541611
FUN_000004 4.017573 3.948293 4.246128 4.485151 3.483284 3.506707 4.045814 3.002747 3.535832 3.619225
FUN_000005 6.143513 6.279209 6.013985 6.332480 6.197335 6.483731 6.774423 6.326885 6.565947 6.418897
FUN_000006 3.464117 3.553942 3.553567 3.765082 3.812964 3.882702 4.015045 3.829788 4.055677 3.878861
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 -3.544860 -3.544860 -2.288180 -2.798684 -2.219245 -3.544860 -2.427624 -3.544860 -3.544860 -2.803442
FUN_000002 -2.694452 -3.544860 -3.544860 -3.544860 -3.083751 -2.984433 -2.880692 -3.544860 -3.544860 -3.544860
FUN_000003 -3.544860 -1.845762 -2.783808 -1.944579 -2.454144 -1.790601 -2.880692 -2.697787 -3.544860 -3.544860
FUN_000004 4.145632 3.935012 4.297898 4.390853 3.483600 3.468274 4.054577 3.047033 3.468780 3.598880
FUN_000005 6.272089 6.265882 6.065929 6.237920 6.197674 6.445061 6.783239 6.371628 6.498468 6.398448
FUN_000006 3.591864 3.540678 3.605186 3.671017 3.813286 3.844206 4.023807 3.874296 3.988478 3.858497
Currently visualizing libraries for pipeline: soft_filtered_kallisto
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 29 42 33 26 44 39 34 29 37 51
FUN_000002 26 36 33 30 10 12 23 5 0 4
FUN_000003 10 19 20 7 8 15 6 22 12 17
FUN_000004 149 175 181 208 190 141 189 136 148 240
FUN_000005 637 920 634 766 1255 1151 1212 1312 1237 1681
16526 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 13.68348 12.97468 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4
FUN_000001 1.6887986 1.8038949 1.744505 1.4831342 1.3749622 1.61244263 1.6294361 0.8525418 1.5049067
FUN_000002 1.5387847 1.5915329 1.744505 1.6794926 -0.5096350 0.05937857 1.0977684 -1.2099441 -2.7743635
FUN_000003 0.2719566 0.7308194 1.062375 -0.2184983 -0.7583979 0.34025532 -0.5856385 0.4906791 0.0274524
FUN_000004 3.9963230 3.8161445 4.147560 4.4155762 3.4215187 3.41582088 4.0472097 2.9870894 3.4480773
FUN_000005 6.0821462 6.1982494 5.947527 6.2891214 6.1283366 6.42749901 6.7173350 6.2331409 6.4941432
FUN_000006 3.5237985 3.6244722 3.560640 3.8593753 3.8632188 3.84102062 3.9290099 3.8532321 4.0330110
E6
FUN_000001 1.38930045
FUN_000002 -1.55575603
FUN_000003 -0.04303163
FUN_000004 3.55893835
FUN_000005 6.35173206
FUN_000006 3.96502259
C4 C5 C2 C3 E2 E3 C6 E1 E4
FUN_000001 1.8027055 1.7964958 1.772086 1.3864811 1.3806353 1.57928698 1.6354343 0.9016173 1.45120631
FUN_000002 1.6521147 1.5841754 1.772086 1.5821482 -0.5050991 0.02933613 1.1036032 -1.1748192 -2.77577728
FUN_000003 0.3770466 0.7237096 1.089164 -0.3029676 -0.7541556 0.30937807 -0.5809764 0.5385083 -0.02109785
FUN_000004 4.1144239 3.8085483 4.176202 4.3141953 3.4275117 3.38151523 4.0535058 3.0395357 3.39223611
FUN_000005 6.2010573 6.1906008 5.976345 6.1872214 6.1344162 6.39278862 6.7236889 6.2864753 6.43764005
FUN_000006 3.6414885 3.6168853 3.589158 3.7583301 3.8692388 3.80659705 3.9353001 3.9061266 3.97691883
E6
FUN_000001 1.37710908
FUN_000002 -1.56366989
FUN_000003 -0.05413738
FUN_000004 3.54624970
FUN_000005 6.33892178
FUN_000006 3.95229907
Currently visualizing libraries for pipeline: soft_filtered_salmon
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 28 40 30 24 40 38 32 27 36 48
FUN_000002 24 35 29 24 9 12 22 4 0 4
FUN_000003 2 3 4 0 0 1 1 2 0 4
FUN_000004 127 153 158 193 168 134 170 121 126 199
FUN_000005 495 730 487 606 980 884 945 1026 980 1322
16526 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 11.83815 11.19564 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.852098 1.967996 1.8655796 1.606875 1.4505435 1.7720015 1.740670 0.9549101 1.667857 1.505951
FUN_000002 1.640914 1.784223 1.8189745 1.606875 -0.4229801 0.2559258 1.232878 -1.2139209 -2.565372 -1.352757
FUN_000003 -1.269677 -1.152989 -0.6613076 -2.565372 -2.5653725 -1.9749715 -1.899156 -1.7368679 -2.565372 -1.352757
FUN_000004 3.979816 3.856693 4.2072412 4.542541 3.4513347 3.5381069 4.089607 3.0177416 3.419353 3.490979
FUN_000005 5.930885 6.097698 5.8222965 6.186113 5.9770733 6.2420082 6.552598 6.0749006 6.358720 6.204327
FUN_000006 3.153011 3.462642 3.3571324 3.560311 3.5334983 3.5274579 3.684906 3.6726690 3.845555 3.696919
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.957936 1.947997 1.8726283 1.501562 1.4666040 1.7515394 1.741928 1.014232 1.611659 1.518373
FUN_000002 1.745976 1.764360 1.8260150 1.501562 -0.4093928 0.2376451 1.234150 -1.173511 -2.563458 -1.344482
FUN_000003 -1.202273 -1.165335 -0.6554462 -2.563458 -2.5634579 -1.9809235 -1.897497 -1.707193 -2.563458 -1.344482
FUN_000004 4.089445 3.835966 4.2144898 4.431534 3.4680908 3.5168160 4.090837 3.081144 3.360812 3.503895
FUN_000005 6.041347 6.076759 5.8295783 6.074522 5.9940208 6.2204250 6.553822 6.139431 6.299317 6.217385
FUN_000006 3.261771 3.442000 3.3643416 3.450140 3.5502672 3.5061696 3.686138 3.736540 3.786761 3.709858
Currently visualizing libraries for pipeline: soft_filtered_strgtieh
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 46 69 49 47 79 76 59 52 64 90
FUN_000002 34 43 50 40 14 19 34 5 0 6
FUN_000003 0 7 0 0 5 4 0 3 0 0
FUN_000004 250 284 299 361 322 261 324 231 237 374
FUN_000005 1036 1425 1017 1209 2053 1950 2023 2160 2042 2645
16526 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 20.8421 19.73807 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.734773 1.943937 1.732630 1.740151 1.5535359 1.85113886 1.7449169 1.020312 1.646831 1.604936
FUN_000002 1.313281 1.283271 1.760943 1.514724 -0.7390255 -0.03810722 0.9797919 -1.827425 -3.381429 -1.774229
FUN_000003 -3.381429 -1.069286 -3.381429 -3.381429 -1.8589948 -1.83240638 -3.3814291 -2.269205 -3.381429 -3.381429
FUN_000004 4.142648 3.957659 4.306664 4.644914 3.5446336 3.60365913 4.1679395 3.117739 3.502936 3.624997
FUN_000005 6.187713 6.277506 6.067819 6.384771 6.2071968 6.49511378 6.8038874 6.328492 6.599125 6.437488
FUN_000006 3.589939 3.704451 3.671518 3.887608 3.8941400 3.93534121 4.0670552 3.966197 4.156607 3.991447
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
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FUN_000006 3.681435 3.698793 3.675058 3.807626 3.9121115 3.91987535 4.0870792 4.012206 4.084107 3.985884
Currently visualizing libraries for pipeline: soft_filtered_strgties
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
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16526 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 21.9297 20.87003 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
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C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
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FUN_000006 3.499842 3.497031 3.540938 3.564843 3.7323102 3.77180805 3.946359 3.8197513 3.929911 3.787572
Currently visualizing libraries for pipeline: unfiltered_htsh
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
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16607 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 21.79658 20.69361 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
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FUN_000005 6.220043 6.3123380 6.0538082 6.4098580 6.2419825 6.542900 6.858461 6.364742 6.665789 6.4897243
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C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 -3.450805 -0.6262679 -0.7505176 -0.5128048 -0.9760856 -1.301912 -1.079185 -2.066231 -1.693835 -0.6015904
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FUN_000005 6.357169 6.3120432 6.1031245 6.3325962 6.2453351 6.499404 6.860788 6.401687 6.589355 6.4580518
FUN_000006 3.843160 3.7970325 3.8251407 3.9436871 4.0312776 4.026074 4.185998 4.133386 4.213205 4.1199776
Currently visualizing libraries for pipeline: unfiltered_htss
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 0 0 2 1 4 0 2 0 0 2
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FUN_000005 1122 1597 1124 1299 2272 2140 2197 2417 2220 2928
16607 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 23.28968 22.01927 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
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FUN_000005 6.143508 6.279195 6.013974 6.332465 6.197329 6.483724 6.774413 6.326881 6.565942 6.418886
FUN_000006 3.464111 3.553929 3.553556 3.765067 3.812958 3.882694 4.015035 3.829784 4.055672 3.878851
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 -3.544792 -3.544792 -2.288806 -2.798798 -2.219015 -3.544792 -2.427515 -3.544792 -3.544792 -2.803280
FUN_000002 -2.694400 -3.544792 -3.544792 -3.544792 -3.083609 -2.984327 -2.880595 -3.544792 -3.544792 -3.544792
FUN_000003 -3.544792 -1.845805 -2.784229 -1.944814 -2.453932 -1.790452 -2.880595 -2.697563 -3.544792 -3.544792
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FUN_000005 6.272120 6.265791 6.064805 6.237535 6.198012 6.445244 6.783382 6.372047 6.498568 6.398749
FUN_000006 3.591895 3.540588 3.604068 3.670635 3.813622 3.844388 4.023950 3.874713 3.988578 3.858796
Currently visualizing libraries for pipeline: unfiltered_kallisto
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 29 42 33 26 44 39 34 29 37 51
FUN_000002 26 36 33 30 10 12 23 5 0 4
FUN_000003 10 19 20 7 8 15 6 22 12 17
FUN_000004 149 175 181 208 190 141 189 136 148 240
FUN_000005 637 920 634 766 1255 1151 1212 1312 1237 1681
16607 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 13.68358 12.9748 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4
FUN_000001 1.6887905 1.8038794 1.744491 1.4831162 1.3749540 1.61243153 1.6294228 0.8525346 1.50489912
FUN_000002 1.5387766 1.5915175 1.744491 1.6794746 -0.5096436 0.05936747 1.0977552 -1.2099523 -2.77437462
FUN_000003 0.2719482 0.7308042 1.062361 -0.2185154 -0.7584066 0.34024423 -0.5856514 0.4906719 0.02744451
FUN_000004 3.9963150 3.8161289 4.147546 4.4155579 3.4215107 3.41580978 4.0471963 2.9870825 3.44806991
FUN_000005 6.0821382 6.1982338 5.947513 6.2891030 6.1283287 6.42748791 6.7173216 6.2331342 6.49413582
FUN_000006 3.5237905 3.6244566 3.560626 3.8593570 3.8632108 3.84100953 3.9289965 3.8532253 4.03300364
E6
FUN_000001 1.38928774
FUN_000002 -1.55576811
FUN_000003 -0.04304418
FUN_000004 3.55892556
FUN_000005 6.35171926
FUN_000006 3.96500981
C4 C5 C2 C3 E2 E3 C6 E1 E4
FUN_000001 1.8026040 1.7964925 1.771523 1.386109 1.3809247 1.57949033 1.6351640 0.9018512 1.45106738
FUN_000002 1.6520140 1.5841726 1.771523 1.581773 -0.5048446 0.02952635 1.1033404 -1.1746298 -2.77570644
FUN_000003 0.3769578 0.7237099 1.088618 -0.303281 -0.7539102 0.30957181 -0.5811853 0.5387383 -0.02121577
FUN_000004 4.1143164 3.8085425 4.175616 4.313801 3.4278110 3.38172343 4.0532218 3.0397803 3.39208843
FUN_000005 6.2009486 6.1905943 5.975755 6.186825 6.1347181 6.39299853 6.7234023 6.2867227 6.43748966
FUN_000006 3.6413816 3.6168796 3.588575 3.757937 3.8695389 3.80680575 3.9350165 3.9063726 3.97677012
E6
FUN_000001 1.37774261
FUN_000002 -1.56326016
FUN_000003 -0.05356071
FUN_000004 3.54690929
FUN_000005 6.33958775
FUN_000006 3.95296049
Currently visualizing libraries for pipeline: unfiltered_salmon
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 28 40 30 24 40 38 32 27 36 48
FUN_000002 24 35 29 24 9 12 22 4 0 4
FUN_000003 2 3 4 0 0 1 1 2 0 4
FUN_000004 127 153 158 193 168 134 170 121 126 199
FUN_000005 495 730 487 606 980 884 945 1026 980 1322
16607 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 11.83823 11.19573 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.852090 1.967983 1.865569 1.606861 1.4505359 1.7719914 1.740658 0.9549038 1.667850 1.505940
FUN_000002 1.640906 1.784211 1.818964 1.606861 -0.4229881 0.2559158 1.232867 -1.2139283 -2.565382 -1.352768
FUN_000003 -1.269686 -1.153001 -0.661318 -2.565382 -2.5653821 -1.9749813 -1.899166 -1.7368759 -2.565382 -1.352768
FUN_000004 3.979808 3.856680 4.207230 4.542527 3.4513272 3.5380968 4.089595 3.0177356 3.419346 3.490967
FUN_000005 5.930878 6.097685 5.822286 6.186098 5.9770659 6.2419981 6.552586 6.0748947 6.358713 6.204315
FUN_000006 3.153004 3.462630 3.357122 3.560297 3.5334909 3.5274478 3.684894 3.6726630 3.845548 3.696908
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.958079 1.947846 1.8728633 1.501280 1.4666787 1.751631 1.741217 1.014309 1.611731 1.518737
FUN_000002 1.746118 1.764210 1.8262497 1.501280 -0.4093273 0.237730 1.233457 -1.173452 -2.563436 -1.344253
FUN_000003 -1.202174 -1.165426 -0.6552604 -2.563436 -2.5634361 -1.980877 -1.897761 -1.707144 -2.563436 -1.344253
FUN_000004 4.089592 3.835809 4.2147331 4.431236 3.4681681 3.516910 4.090095 3.081226 3.360886 3.504275
FUN_000005 6.041495 6.076600 5.8298229 6.074222 5.9940989 6.220520 6.553074 6.139514 6.299392 6.217769
FUN_000006 3.261918 3.441844 3.3645832 3.449844 3.5503446 3.506264 3.685398 3.736622 3.786835 3.710239
Currently visualizing libraries for pipeline: unfiltered_strgtieh
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 46 69 49 47 79 76 59 52 64 90
FUN_000002 34 43 50 40 14 19 34 5 0 6
FUN_000003 0 7 0 0 5 4 0 3 0 0
FUN_000004 250 284 299 361 322 261 324 231 237 374
FUN_000005 1036 1425 1017 1209 2053 1950 2023 2160 2042 2645
16607 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 20.84221 19.73819 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.734767 1.943924 1.732622 1.740139 1.5535311 1.85113029 1.7449093 1.020308 1.646826 1.604927
FUN_000002 1.313274 1.283259 1.760935 1.514712 -0.7390307 -0.03811572 0.9797843 -1.827430 -3.381437 -1.774238
FUN_000003 -3.381437 -1.069297 -3.381437 -3.381437 -1.8590005 -1.83241463 -3.3814366 -2.269211 -3.381437 -3.381437
FUN_000004 4.142641 3.957647 4.306656 4.644902 3.5446289 3.60365054 4.1679320 3.117735 3.502931 3.624988
FUN_000005 6.187707 6.277494 6.067811 6.384759 6.2071921 6.49510518 6.8038798 6.328488 6.599120 6.437479
FUN_000006 3.589932 3.704439 3.671510 3.887597 3.8941353 3.93533261 4.0670477 3.966192 4.156602 3.991438
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.824369 1.938344 1.736061 1.661973 1.5710855 1.83585864 1.7645157 1.064406 1.576263 1.599647
FUN_000002 1.402002 1.277763 1.764376 1.436949 -0.7237622 -0.05224458 0.9989915 -1.796728 -3.381320 -1.777862
FUN_000003 -3.381320 -1.073845 -3.381320 -3.381320 -1.8471088 -1.84268617 -3.3813198 -2.244119 -3.381320 -3.381320
FUN_000004 4.234333 3.951957 4.310177 4.564673 3.5626216 3.58808160 4.1880024 3.163481 3.430710 3.619575
FUN_000005 6.279765 6.271775 6.071344 6.304307 6.2253089 6.47942765 6.8240395 6.374681 6.526340 6.432028
FUN_000006 3.681397 3.698755 3.675022 3.807587 3.9121597 3.91973789 4.0871104 4.012161 4.084151 3.986015
Currently visualizing libraries for pipeline: unfiltered_strgties
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
An object of class "DGEList"
$counts
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 53 64 54 42 76 77 66 51 65 91
FUN_000002 45 63 47 50 19 22 43 7 0 6
FUN_000003 0 0 0 0 3 4 3 0 0 0
FUN_000004 241 273 287 341 318 258 307 221 231 356
FUN_000005 1021 1409 1010 1166 2079 1994 2024 2187 2023 2599
16607 more rows ...
$samplesNA
Mean and Median Library Size in Millions: 21.92982 20.87014 Warning in brewer.pal(nlevels(col.group), "Set1") :
minimal value for n is 3, returning requested palette with 3 different levels
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.872225 1.791198 1.805471 1.540503 1.421741 1.76151751 1.802771 0.9084147 1.586880 1.567103
FUN_000002 1.642533 1.769081 1.610768 1.784786 -0.437972 0.04805507 1.204674 -1.5721480 -3.454823 -1.833642
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FUN_000004 4.028882 3.854554 4.184603 4.521598 3.448830 3.47847366 3.990559 2.9689364 3.384438 3.501604
FUN_000005 6.105589 6.214900 5.994647 6.291264 6.147378 6.41838226 6.704419 6.2605851 6.503765 6.359551
FUN_000006 3.387326 3.512441 3.523773 3.674812 3.722563 3.79581515 3.928503 3.7662895 3.983702 3.795205
C4 C5 C2 C3 E2 E3 C6 E1 E4 E6
FUN_000001 1.982824 1.776361 1.821980 1.433410 1.4313182 1.73800875 1.820165 0.9598587 1.534410 1.559501
FUN_000002 1.752662 1.754250 1.627213 1.677134 -0.4292879 0.02603313 1.221828 -1.5325971 -3.454913 -1.838959
FUN_000003 -3.454913 -3.454913 -3.454913 -3.454913 -2.3654829 -1.94484703 -2.014156 -3.4549133 -3.454913 -3.454913
FUN_000004 4.141591 3.839411 4.201469 4.411362 3.4586632 3.45451074 4.008317 3.0223447 3.330778 3.493825
FUN_000005 6.218763 6.199681 6.011574 6.180705 6.1572820 6.39424663 6.722264 6.3145491 6.449680 6.351718
FUN_000006 3.499694 3.497324 3.540590 3.564941 3.7324108 3.77181303 3.946256 3.8199607 3.929879 3.787415
Currently proccessing: hard_filtered_htsh with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 14301 10
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000004 329.06335 0.8021298 0.145444 5.515035 3.48711e-08 6.38189e-07
FUN_000005 1790.98771 -0.0162546 0.153936 -0.105593 9.15905e-01 9.54708e-01
FUN_000006 335.22436 -0.1696714 0.113818 -1.490731 1.36032e-01 2.70729e-01
FUN_000007 2491.40424 0.7485811 0.132787 5.637458 1.72578e-08 3.52568e-07
FUN_000008 204.25403 0.6721664 0.313293 2.145488 3.19139e-02 8.78170e-02
FUN_000009 8.50932 -0.4926493 0.560952 -0.878238 3.79815e-01 5.54059e-01
out of 13177 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2694, 20%
LFC < 0 (down) : 2217, 17%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4172 Min. : 2 Length:4172 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 157 Class :character 1st Qu.:1.068e-06 1st Qu.:1.348e-05
Mode :character Median : 441 Mode :character Median :2.280e-04 Median :1.440e-03
Mean : 2815 Mean :2.288e-03 Mean :8.429e-03
3rd Qu.: 1184 3rd Qu.:2.950e-03 3rd Qu.:1.242e-02
Max. :3264274 Max. :1.576e-02 Max. :4.978e-02 
Currently proccessing: hard_filtered_htss with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 14301 10
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
-- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000004 317.8861 0.7586462 0.132790 5.713123 1.10921e-08 1.83242e-07
FUN_000005 1829.7844 -0.0319764 0.138075 -0.231588 8.16858e-01 8.88088e-01
FUN_000006 305.1968 -0.1707248 0.110897 -1.539484 1.23686e-01 2.34155e-01
FUN_000007 2468.9024 0.7468472 0.120586 6.193477 5.88513e-10 1.28135e-08
FUN_000008 230.9897 0.6546024 0.290035 2.256975 2.40096e-02 6.36277e-02
FUN_000009 86.8491 -0.3421158 0.184391 -1.855384 6.35414e-02 1.39172e-01
out of 13216 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2969, 22%
LFC < 0 (down) : 2551, 19%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4730 Min. : 3 Length:4730 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 176 Class :character 1st Qu.:6.020e-07 1st Qu.:6.720e-06
Mode :character Median : 473 Mode :character Median :1.993e-04 Median :1.114e-03
Mean : 2898 Mean :2.458e-03 Mean :7.952e-03
3rd Qu.: 1180 3rd Qu.:3.039e-03 3rd Qu.:1.132e-02
Max. :3487633 Max. :1.788e-02 Max. :4.995e-02
Currently proccessing: hard_filtered_kallisto with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 14301 10
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
-- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000004 178.117 0.7474916 0.1308573 5.712266 1.11482e-08 1.81571e-07
FUN_000005 1029.223 -0.0340947 0.1402233 -0.243146 8.07893e-01 8.78819e-01
FUN_000006 181.801 -0.1820578 0.0979736 -1.858233 6.31360e-02 1.37388e-01
FUN_000007 1428.859 0.7308336 0.1110079 6.583618 4.59135e-11 1.20684e-09
FUN_000008 128.625 0.7024505 0.2762820 2.542513 1.10059e-02 3.29036e-02
FUN_000009 37.374 -0.2486318 0.2426558 -1.024627 3.05539e-01 4.56551e-01
out of 13274 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 3000, 23%
LFC < 0 (down) : 2622, 20%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4820 Min. : 2.8 Length:4820 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 115.7 Class :character 1st Qu.:5.030e-07 1st Qu.:5.540e-06
Mode :character Median : 286.4 Mode :character Median :1.653e-04 Median :9.102e-04
Mean : 1686.1 Mean :2.494e-03 Mean :7.900e-03
3rd Qu.: 684.4 3rd Qu.:2.936e-03 3rd Qu.:1.078e-02
Max. :2105672.7 Max. :1.815e-02 Max. :4.998e-02
Currently proccessing: hard_filtered_salmon with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 14301 10
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
-- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000004 157.4772 0.7682161 0.138521 5.545853 2.92525e-08 4.54468e-07
FUN_000005 803.6924 -0.0297947 0.137036 -0.217422 8.27879e-01 8.95893e-01
FUN_000006 131.0924 -0.2010878 0.118626 -1.695146 9.00476e-02 1.85799e-01
FUN_000007 1361.2013 0.7239549 0.117415 6.165802 7.01267e-10 1.52781e-08
FUN_000008 113.9014 0.7237316 0.280859 2.576848 9.97059e-03 3.14485e-02
FUN_000009 45.0502 -0.2687808 0.217626 -1.235060 2.16808e-01 3.61640e-01
out of 13159 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2847, 22%
LFC < 0 (down) : 2491, 19%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4570 Min. : 0.9 Length:4570 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 95.5 Class :character 1st Qu.:5.520e-07 1st Qu.:6.350e-06
Mode :character Median : 241.2 Mode :character Median :1.860e-04 Median :1.071e-03
Mean : 1521.7 Mean :2.475e-03 Mean :8.252e-03
3rd Qu.: 594.2 3rd Qu.:3.127e-03 3rd Qu.:1.200e-02
Max. :1999047.9 Max. :1.735e-02 Max. :4.997e-02
Currently proccessing: hard_filtered_strgtieh with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 14301 10
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000004 301.090 0.7965256 0.147325 5.406573 6.42420e-08 1.08756e-06
FUN_000005 1671.825 -0.0272128 0.145128 -0.187509 8.51262e-01 9.15985e-01
FUN_000006 294.373 -0.1734708 0.111659 -1.553579 1.20285e-01 2.43667e-01
FUN_000007 2354.575 0.7151740 0.120552 5.932493 2.98369e-09 7.12530e-08
FUN_000008 200.577 0.6417664 0.303446 2.114930 3.44359e-02 9.22481e-02
FUN_000009 18.840 -0.1824421 0.331933 -0.549635 5.82570e-01 7.29573e-01
out of 13137 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2684, 20%
LFC < 0 (down) : 2330, 18%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4234 Min. : 3 Length:4234 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 176 Class :character 1st Qu.:1.038e-06 1st Qu.:1.287e-05
Mode :character Median : 447 Mode :character Median :2.157e-04 Median :1.338e-03
Mean : 2877 Mean :2.421e-03 Mean :8.749e-03
3rd Qu.: 1129 3rd Qu.:3.198e-03 3rd Qu.:1.323e-02
Max. :3270758 Max. :1.611e-02 Max. :4.998e-02
Currently proccessing: hard_filtered_strgties with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 14301 10
metadata(1): version
assays(1): counts
rownames(14301): FUN_000004 FUN_000005 ... FUN_016610 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000004 289.7407 0.7837389 0.150092 5.221723 1.77266e-07 2.71122e-06
FUN_000005 1662.5022 -0.0392133 0.143688 -0.272906 7.84926e-01 8.71523e-01
FUN_000006 273.6608 -0.1774747 0.121593 -1.459583 1.44405e-01 2.76800e-01
FUN_000007 2320.1885 0.7229205 0.120880 5.980497 2.22458e-09 5.38144e-08
FUN_000008 215.1178 0.6694007 0.300142 2.230283 2.57287e-02 7.29854e-02
FUN_000009 86.5654 -0.2931142 0.178859 -1.638802 1.01255e-01 2.13283e-01
out of 13184 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2724, 21%
LFC < 0 (down) : 2328, 18%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4265 Min. : 5 Length:4265 Min. :0.000e+00 Min. :0.0000000
Class :character 1st Qu.: 195 Class :character 1st Qu.:1.101e-06 1st Qu.:0.0000136
Mode :character Median : 471 Mode :character Median :2.302e-04 Median :0.0014231
Mean : 2980 Mean :2.443e-03 Mean :0.0088114
3rd Qu.: 1159 3rd Qu.:3.243e-03 3rd Qu.:0.0133647
Max. :3399678 Max. :1.617e-02 Max. :0.0499740
Currently proccessing: pipeline_filtered_htsh with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 13978 10
metadata(1): version
assays(1): counts
rownames(13978): FUN_000001 FUN_000002 ... FUN_016606 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 7.71570 0.3181218 0.651703 0.4881395 6.25451e-01 7.66555e-01
FUN_000002 4.53848 3.7767524 0.991774 3.8080784 1.40051e-04 9.94731e-04
FUN_000003 2.97278 1.2457417 1.083952 1.1492589 2.50449e-01 4.25471e-01
FUN_000004 328.92849 0.8029741 0.145114 5.5334067 3.14070e-08 6.08043e-07
FUN_000005 1790.29090 -0.0151639 0.154915 -0.0978855 9.22023e-01 9.57454e-01
FUN_000006 335.07379 -0.1686332 0.113899 -1.4805514 1.38726e-01 2.80185e-01
out of 13978 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2768, 20%
LFC < 0 (down) : 2259, 16%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4238 Min. : 2 Length:4238 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 141 Class :character 1st Qu.:1.076e-06 1st Qu.:1.419e-05
Mode :character Median : 418 Mode :character Median :2.286e-04 Median :1.507e-03
Mean : 2764 Mean :2.209e-03 Mean :8.508e-03
3rd Qu.: 1160 3rd Qu.:2.873e-03 3rd Qu.:1.264e-02
Max. :3262641 Max. :1.515e-02 Max. :4.997e-02
Currently proccessing: pipeline_filtered_htss with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 14331 10
metadata(1): version
assays(1): counts
rownames(14331): FUN_000004 FUN_000005 ... FUN_016606 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000004 317.6672 0.7591278 0.145682 5.210856 1.87971e-07 2.97659e-06
FUN_000005 1828.8132 -0.0317301 0.150706 -0.210543 8.33244e-01 9.04569e-01
FUN_000006 305.0215 -0.1709978 0.123492 -1.384683 1.66149e-01 3.13672e-01
FUN_000007 2467.3683 0.7469253 0.129643 5.761388 8.34252e-09 1.83933e-07
FUN_000008 230.8187 0.6539446 0.309705 2.111507 3.47287e-02 9.58954e-02
FUN_000009 86.8004 -0.3426517 0.192893 -1.776385 7.56695e-02 1.75757e-01
out of 14331 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2888, 20%
LFC < 0 (down) : 2363, 16%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4490 Min. : 2 Length:4490 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 151 Class :character 1st Qu.:1.287e-06 1st Qu.:1.643e-05
Mode :character Median : 440 Mode :character Median :2.490e-04 Median :1.589e-03
Mean : 2970 Mean :2.462e-03 Mean :9.222e-03
3rd Qu.: 1163 3rd Qu.:3.434e-03 3rd Qu.:1.461e-02
Max. :3485148 Max. :1.564e-02 Max. :4.991e-02
Currently proccessing: pipeline_filtered_kallisto with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 14434 10
metadata(1): version
assays(1): counts
rownames(14434): FUN_000001 FUN_000002 ... FUN_016606 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 35.8632 0.3614205 0.214507 1.684891 9.20096e-02 2.03006e-01
FUN_000002 20.8052 2.8891732 0.481351 6.002214 1.94645e-09 4.84398e-08
FUN_000003 13.4965 0.3815455 0.446491 0.854542 3.92805e-01 5.71144e-01
FUN_000004 177.9485 0.7472602 0.143653 5.201831 1.97335e-07 3.07595e-06
FUN_000005 1028.6181 -0.0347702 0.151032 -0.230217 8.17924e-01 8.93778e-01
FUN_000006 181.6794 -0.1830192 0.110417 -1.657530 9.74124e-02 2.12010e-01
out of 14434 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2887, 20%
LFC < 0 (down) : 2438, 17%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4499 Min. : 2.5 Length:4499 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 103.5 Class :character 1st Qu.:9.970e-07 1st Qu.:1.279e-05
Mode :character Median : 274.1 Mode :character Median :1.999e-04 Median :1.282e-03
Mean : 1762.0 Mean :2.277e-03 Mean :8.496e-03
3rd Qu.: 690.3 3rd Qu.:2.867e-03 3rd Qu.:1.226e-02
Max. :2104523.3 Max. :1.554e-02 Max. :4.987e-02
Currently proccessing: pipeline_filtered_salmon with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 13668 10
metadata(1): version
assays(1): counts
rownames(13668): FUN_000001 FUN_000002 ... FUN_016606 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
-- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 33.8100 0.3565179 0.210599 1.692879 9.04784e-02 1.88573e-01
FUN_000002 18.9081 2.8462956 0.444522 6.403043 1.52310e-10 3.86229e-09
FUN_000004 157.4409 0.7678860 0.138167 5.557681 2.73383e-08 4.43777e-07
FUN_000005 803.5784 -0.0300373 0.137189 -0.218949 8.26690e-01 8.95457e-01
FUN_000006 131.0752 -0.2014107 0.118556 -1.698870 8.93436e-02 1.86663e-01
FUN_000007 1360.9607 0.7236389 0.117767 6.144678 8.01259e-10 1.78075e-08
out of 13668 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2917, 21%
LFC < 0 (down) : 2529, 19%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4656 Min. : 2.0 Length:4656 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 88.4 Class :character 1st Qu.:6.090e-07 1st Qu.:7.150e-06
Mode :character Median : 235.3 Mode :character Median :1.824e-04 Median :1.071e-03
Mean : 1469.8 Mean :2.417e-03 Mean :8.218e-03
3rd Qu.: 583.4 3rd Qu.:3.030e-03 3rd Qu.:1.186e-02
Max. :1998822.1 Max. :1.703e-02 Max. :4.998e-02
Currently proccessing: pipeline_filtered_strgtieh with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 13581 10
metadata(1): version
assays(1): counts
rownames(13581): FUN_000001 FUN_000002 ... FUN_016607 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 61.6835 0.2616064 0.196726 1.329800 1.83584e-01 3.36933e-01
FUN_000002 28.8965 2.8703467 0.616942 4.652536 3.27877e-06 3.62614e-05
FUN_000004 301.0680 0.7965431 0.146957 5.420249 5.95160e-08 1.04027e-06
FUN_000005 1671.7991 -0.0270175 0.145279 -0.185969 8.52469e-01 9.17747e-01
FUN_000006 294.3629 -0.1733382 0.111632 -1.552760 1.20480e-01 2.46906e-01
FUN_000007 2354.5175 0.7152987 0.120593 5.931516 3.00150e-09 7.25327e-08
out of 13581 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2713, 20%
LFC < 0 (down) : 2363, 17%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4286 Min. : 3 Length:4286 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 170 Class :character 1st Qu.:1.040e-06 1st Qu.:1.317e-05
Mode :character Median : 438 Mode :character Median :2.169e-04 Median :1.374e-03
Mean : 2841 Mean :2.370e-03 Mean :8.753e-03
3rd Qu.: 1113 3rd Qu.:3.143e-03 3rd Qu.:1.328e-02
Max. :3270732 Max. :1.574e-02 Max. :4.988e-02
Currently proccessing: pipeline_filtered_strgties with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 14002 10
metadata(1): version
assays(1): counts
rownames(14002): FUN_000001 FUN_000002 ... FUN_016607 FUN_016608
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 62.9472 0.3300023 0.208950 1.579335 1.14259e-01 2.39622e-01
FUN_000002 35.5303 2.8927390 0.618695 4.675546 2.93172e-06 3.33468e-05
FUN_000004 289.7479 0.7839518 0.149676 5.237663 1.62623e-07 2.60830e-06
FUN_000005 1662.6752 -0.0387563 0.143834 -0.269451 7.87583e-01 8.77971e-01
FUN_000006 273.6803 -0.1770701 0.121549 -1.456783 1.45176e-01 2.84926e-01
FUN_000007 2320.3713 0.7232859 0.120562 5.999299 1.98171e-09 5.11955e-08
out of 14002 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2779, 20%
LFC < 0 (down) : 2359, 17%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4333 Min. : 2 Length:4333 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 185 Class :character 1st Qu.:1.106e-06 1st Qu.:1.429e-05
Mode :character Median : 456 Mode :character Median :2.165e-04 Median :1.399e-03
Mean : 2933 Mean :2.323e-03 Mean :8.752e-03
3rd Qu.: 1142 3rd Qu.:3.026e-03 3rd Qu.:1.304e-02
Max. :3399846 Max. :1.546e-02 Max. :4.995e-02
Currently proccessing: soft_filtered_htsh with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16531 10
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 7.71570 0.3181787 0.643799 0.4942207 6.21150e-01 7.93285e-01
FUN_000002 4.53848 3.7775642 0.983005 3.8428741 1.21602e-04 9.47956e-04
FUN_000003 2.97278 1.2464273 1.071784 1.1629461 2.44851e-01 4.45155e-01
FUN_000004 328.92849 0.8029715 0.145023 5.5368447 3.07969e-08 6.50258e-07
FUN_000005 1790.29090 -0.0151654 0.155204 -0.0977128 9.22160e-01 9.67069e-01
FUN_000006 335.07379 -0.1686308 0.113793 -1.4819122 1.38364e-01 2.98634e-01
out of 15139 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2752, 18%
LFC < 0 (down) : 2232, 15%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4210 Min. : 2 Length:4210 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 137 Class :character 1st Qu.:1.017e-06 1st Qu.:1.462e-05
Mode :character Median : 415 Mode :character Median :2.178e-04 Median :1.566e-03
Mean : 2771 Mean :2.052e-03 Mean :8.622e-03
3rd Qu.: 1153 3rd Qu.:2.670e-03 3rd Qu.:1.280e-02
Max. :3262641 Max. :1.387e-02 Max. :4.987e-02
Currently proccessing: soft_filtered_htss with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16531 10
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 1.028317 0.5390259 1.632370 0.330211 7.41241e-01 8.61283e-01
FUN_000002 0.426296 0.6951703 2.121768 0.327637 7.43186e-01 8.62669e-01
FUN_000003 1.876808 0.4266906 1.204549 0.354233 7.23164e-01 8.50533e-01
FUN_000004 317.673129 0.7589270 0.145429 5.218550 1.80329e-07 3.05719e-06
FUN_000005 1828.906247 -0.0319145 0.150734 -0.211727 8.32320e-01 9.16397e-01
FUN_000006 305.037785 -0.1711777 0.123180 -1.389653 1.64634e-01 3.28924e-01
out of 15292 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2861, 19%
LFC < 0 (down) : 2328, 15%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4441 Min. : 1 Length:4441 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 149 Class :character 1st Qu.:1.129e-06 1st Qu.:1.552e-05
Mode :character Median : 438 Mode :character Median :2.269e-04 Median :1.562e-03
Mean : 2993 Mean :2.280e-03 Mean :9.211e-03
3rd Qu.: 1166 3rd Qu.:3.199e-03 3rd Qu.:1.469e-02
Max. :3485388 Max. :1.451e-02 Max. :4.996e-02
Currently proccessing: soft_filtered_kallisto with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16531 10
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 35.8637 0.361341 0.215564 1.676262 9.36868e-02 2.17619e-01
FUN_000002 20.8053 2.888951 0.484318 5.964994 2.44644e-09 6.33108e-08
FUN_000003 13.4968 0.381483 0.448651 0.850289 3.95164e-01 5.97367e-01
FUN_000004 177.9504 0.747244 0.143893 5.193061 2.06864e-07 3.42663e-06
FUN_000005 1028.6291 -0.034806 0.150954 -0.230573 8.17646e-01 9.06687e-01
FUN_000006 181.6816 -0.183051 0.110725 -1.653202 9.82898e-02 2.25442e-01
out of 15372 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2840, 18%
LFC < 0 (down) : 2401, 16%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4434 Min. : 1.4 Length:4434 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 103.5 Class :character 1st Qu.:9.360e-07 1st Qu.:1.298e-05
Mode :character Median : 275.8 Mode :character Median :1.833e-04 Median :1.270e-03
Mean : 1779.3 Mean :2.079e-03 Mean :8.384e-03
3rd Qu.: 696.5 3rd Qu.:2.644e-03 3rd Qu.:1.222e-02
Max. :2104542.5 Max. :1.442e-02 Max. :4.999e-02
Currently proccessing: soft_filtered_salmon with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16531 10
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
-- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 33.81217 0.3565438 0.210528 1.693566 9.03478e-02 2.04178e-01
FUN_000002 18.90942 2.8460602 0.446937 6.367916 1.91614e-10 5.28854e-09
FUN_000003 1.74844 1.2352167 0.967884 1.276203 2.01884e-01 3.71830e-01
FUN_000004 157.45127 0.7678994 0.137988 5.564971 2.62196e-08 4.71063e-07
FUN_000005 803.62963 -0.0300216 0.137203 -0.218811 8.26797e-01 9.10615e-01
FUN_000006 131.08377 -0.2013792 0.118380 -1.701125 8.89195e-02 2.01860e-01
out of 15042 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2915, 19%
LFC < 0 (down) : 2482, 17%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4599 Min. : 0.7 Length:4599 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 87.0 Class :character 1st Qu.:5.240e-07 1st Qu.:6.850e-06
Mode :character Median : 232.8 Mode :character Median :1.662e-04 Median :1.087e-03
Mean : 1477.7 Mean :2.174e-03 Mean :8.244e-03
3rd Qu.: 579.6 3rd Qu.:2.783e-03 3rd Qu.:1.214e-02
Max. :1998942.6 Max. :1.528e-02 Max. :4.999e-02
Currently proccessing: soft_filtered_strgtieh with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16531 10
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 61.68424 0.2615620 0.197708 1.322973 1.85844e-01 3.57396e-01
FUN_000002 28.89694 2.8701988 0.625310 4.590042 4.43157e-06 5.01203e-05
FUN_000003 1.70305 -0.3083270 2.289997 -0.134641 8.92896e-01 9.46094e-01
FUN_000004 301.07182 0.7965472 0.147053 5.416752 6.06915e-08 1.13030e-06
FUN_000005 1671.81633 -0.0270183 0.144814 -0.186572 8.51997e-01 9.25908e-01
FUN_000006 294.36623 -0.1733301 0.111472 -1.554926 1.19964e-01 2.60692e-01
out of 14601 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2702, 19%
LFC < 0 (down) : 2351, 16%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4256 Min. : 2 Length:4256 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 166 Class :character 1st Qu.:9.380e-07 1st Qu.:1.287e-05
Mode :character Median : 435 Mode :character Median :1.969e-04 Median :1.350e-03
Mean : 2846 Mean :2.163e-03 Mean :8.646e-03
3rd Qu.: 1107 3rd Qu.:2.815e-03 3rd Qu.:1.288e-02
Max. :3270757 Max. :1.455e-02 Max. :4.991e-02
Currently proccessing: soft_filtered_strgties with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16531 10
metadata(1): version
assays(1): counts
rownames(16531): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 62.947201 0.3299902 0.210155 1.570224 1.16363e-01 2.54849e-01
FUN_000002 35.530287 2.8926589 0.627913 4.606779 4.08954e-06 4.68381e-05
FUN_000003 0.897157 -0.7209629 2.993673 -0.240829 8.09688e-01 8.99524e-01
FUN_000004 289.747892 0.7839511 0.149653 5.238472 1.61912e-07 2.73830e-06
FUN_000005 1662.675153 -0.0387502 0.143192 -0.270616 7.86686e-01 8.86032e-01
FUN_000006 273.680295 -0.1770550 0.121315 -1.459468 1.44436e-01 2.97491e-01
out of 14912 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2771, 19%
LFC < 0 (down) : 2359, 16%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4339 Min. : 2 Length:4339 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 182 Class :character 1st Qu.:1.024e-06 1st Qu.:1.407e-05
Mode :character Median : 453 Mode :character Median :2.071e-04 Median :1.423e-03
Mean : 2926 Mean :2.223e-03 Mean :8.906e-03
3rd Qu.: 1135 3rd Qu.:2.869e-03 3rd Qu.:1.314e-02
Max. :3399846 Max. :1.455e-02 Max. :5.000e-02
Currently proccessing: unfiltered_htsh with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16612 10
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 7.71570 0.3181713 0.644834 0.4934158 6.21719e-01 7.94706e-01
FUN_000002 4.53848 3.7774501 0.984221 3.8380113 1.24035e-04 9.70596e-04
FUN_000003 2.97278 1.2463381 1.073345 1.1611717 2.45572e-01 4.47510e-01
FUN_000004 328.92849 0.8029733 0.145086 5.5344599 3.12189e-08 6.59933e-07
FUN_000005 1790.29090 -0.0151654 0.155200 -0.0977153 9.22158e-01 9.67238e-01
FUN_000006 335.07379 -0.1686328 0.113881 -1.4807774 1.38666e-01 3.00512e-01
out of 15220 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2752, 18%
LFC < 0 (down) : 2229, 15%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4211 Min. : 2 Length:4211 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 137 Class :character 1st Qu.:1.022e-06 1st Qu.:1.477e-05
Mode :character Median : 415 Mode :character Median :2.186e-04 Median :1.580e-03
Mean : 2770 Mean :2.052e-03 Mean :8.671e-03
3rd Qu.: 1153 3rd Qu.:2.674e-03 3rd Qu.:1.288e-02
Max. :3262641 Max. :1.383e-02 Max. :4.999e-02
Currently proccessing: unfiltered_htss with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16612 10
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 1.028317 0.5391401 1.634638 0.329822 7.41534e-01 8.62364e-01
FUN_000002 0.426296 0.6951695 2.126042 0.326978 7.43684e-01 8.63951e-01
FUN_000003 1.876808 0.4265735 1.205931 0.353729 7.23542e-01 8.51815e-01
FUN_000004 317.673129 0.7589285 0.145485 5.216546 1.82290e-07 3.09994e-06
FUN_000005 1828.906247 -0.0319145 0.150739 -0.211721 8.32325e-01 9.17228e-01
FUN_000006 305.037785 -0.1711848 0.123349 -1.387813 1.65194e-01 3.30929e-01
out of 15373 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2865, 19%
LFC < 0 (down) : 2327, 15%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4438 Min. : 1 Length:4438 Min. :0.000e+00 Min. :0.0000000
Class :character 1st Qu.: 149 Class :character 1st Qu.:1.112e-06 1st Qu.:0.0000154
Mode :character Median : 436 Mode :character Median :2.263e-04 Median :0.0015678
Mean : 2991 Mean :2.268e-03 Mean :0.0092173
3rd Qu.: 1165 3rd Qu.:3.174e-03 3rd Qu.:0.0146589
Max. :3485388 Max. :1.439e-02 Max. :0.0498517
Currently proccessing: unfiltered_kallisto with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16612 10
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 35.8637 0.3613187 0.216018 1.672631 9.44000e-02 2.19977e-01
FUN_000002 20.8053 2.8888583 0.485602 5.949021 2.69751e-09 6.97069e-08
FUN_000003 13.4968 0.3814586 0.449545 0.848544 3.96135e-01 5.99740e-01
FUN_000004 177.9504 0.7472523 0.144029 5.188218 2.12316e-07 3.53166e-06
FUN_000005 1028.6291 -0.0348062 0.150975 -0.230544 8.17669e-01 9.07393e-01
FUN_000006 181.6816 -0.1830523 0.110887 -1.650805 9.87784e-02 2.27451e-01
out of 15453 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2835, 18%
LFC < 0 (down) : 2395, 15%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4428 Min. : 1.4 Length:4428 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 103.5 Class :character 1st Qu.:9.540e-07 1st Qu.:1.331e-05
Mode :character Median : 275.8 Mode :character Median :1.831e-04 Median :1.278e-03
Mean : 1779.8 Mean :2.066e-03 Mean :8.387e-03
3rd Qu.: 695.6 3rd Qu.:2.613e-03 3rd Qu.:1.216e-02
Max. :2104542.5 Max. :1.429e-02 Max. :4.986e-02
Currently proccessing: unfiltered_salmon with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16612 10
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 33.81235 0.3554540 0.225872 1.573699 1.15557e-01 2.63744e-01
FUN_000002 18.90947 2.8409739 0.500820 5.672644 1.40610e-08 3.21215e-07
FUN_000003 1.74846 1.2563643 1.131258 1.110591 2.66745e-01 4.76717e-01
FUN_000004 157.45178 0.7684704 0.152846 5.027748 4.96272e-07 7.71339e-06
FUN_000005 803.63155 -0.0302264 0.147730 -0.204606 8.37880e-01 9.25721e-01
FUN_000006 131.08428 -0.2023537 0.131968 -1.533355 1.25189e-01 2.80353e-01
out of 15123 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2661, 18%
LFC < 0 (down) : 2217, 15%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4138 Min. : 1.2 Length:4138 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 90.2 Class :character 1st Qu.:7.900e-07 1st Qu.:1.154e-05
Mode :character Median : 238.1 Mode :character Median :1.977e-04 Median :1.444e-03
Mean : 1605.7 Mean :2.129e-03 Mean :9.081e-03
3rd Qu.: 614.6 3rd Qu.:2.809e-03 3rd Qu.:1.369e-02
Max. :1998955.1 Max. :1.366e-02 Max. :4.993e-02
Currently proccessing: unfiltered_strgtieh with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16612 10
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 61.68424 0.2615580 0.197798 1.322349 1.86052e-01 3.59328e-01
FUN_000002 28.89694 2.8702001 0.625243 4.590534 4.42114e-06 5.02408e-05
FUN_000003 1.70305 -0.3083201 2.289714 -0.134654 8.92885e-01 9.46454e-01
FUN_000004 301.07182 0.7965483 0.147091 5.415337 6.11733e-08 1.14413e-06
FUN_000005 1671.81633 -0.0270184 0.144836 -0.186545 8.52017e-01 9.26700e-01
FUN_000006 294.36623 -0.1733332 0.111531 -1.554130 1.20153e-01 2.62280e-01
out of 14682 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2702, 18%
LFC < 0 (down) : 2344, 16%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4256 Min. : 2 Length:4256 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 165 Class :character 1st Qu.:9.310e-07 1st Qu.:1.284e-05
Mode :character Median : 433 Mode :character Median :1.977e-04 Median :1.363e-03
Mean : 2845 Mean :2.158e-03 Mean :8.673e-03
3rd Qu.: 1106 3rd Qu.:2.819e-03 3rd Qu.:1.297e-02
Max. :3270757 Max. :1.448e-02 Max. :4.996e-02
Currently proccessing: unfiltered_strgties with DESeq
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factorsclass: DESeqDataSet
dim: 16612 10
metadata(1): version
assays(1): counts
rownames(16612): FUN_000001 FUN_000002 ... FUN_016611 FUN_016612
rowData names(0):
colnames(10): C4 C5 ... E4 E6
colData names(3): SRRID SAMPNAME Treat
Results Below: estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testinglog2 fold change (MLE): Treat Restricted vs AdLib
Wald test p-value: Treat Restricted vs AdLib
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
FUN_000001 62.947201 0.3299895 0.210220 1.569731 1.16478e-01 2.56088e-01
FUN_000002 35.530287 2.8926593 0.627866 4.607125 4.08274e-06 4.70143e-05
FUN_000003 0.897157 -0.7209628 2.993668 -0.240829 8.09687e-01 9.00118e-01
FUN_000004 289.747892 0.7839520 0.149683 5.237412 1.62844e-07 2.76816e-06
FUN_000005 1662.675153 -0.0387503 0.143209 -0.270585 7.86710e-01 8.86720e-01
FUN_000006 273.680295 -0.1770577 0.121357 -1.458988 1.44568e-01 2.98926e-01
out of 14993 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2769, 18%
LFC < 0 (down) : 2356, 16%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
NULL GeneID meanExpr logFC pval adj.pval
Length:4332 Min. : 2 Length:4332 Min. :0.000e+00 Min. :0.000e+00
Class :character 1st Qu.: 181 Class :character 1st Qu.:1.015e-06 1st Qu.:1.404e-05
Mode :character Median : 453 Mode :character Median :2.047e-04 Median :1.417e-03
Mean : 2930 Mean :2.197e-03 Mean :8.865e-03
3rd Qu.: 1136 3rd Qu.:2.849e-03 3rd Qu.:1.314e-02
Max. :3399846 Max. :1.444e-02 Max. :4.996e-02
Currently proccessing: hard_filtered_htsh with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2001
Currently proccessing: hard_filtered_htss with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 1996
Currently proccessing: hard_filtered_kallisto with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2155
Currently proccessing: hard_filtered_salmon with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2116
Currently proccessing: hard_filtered_strgtieh with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 1904
Currently proccessing: hard_filtered_strgties with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 1936
Currently proccessing: pipeline_filtered_htsh with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2101
Currently proccessing: pipeline_filtered_htss with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2157
Currently proccessing: pipeline_filtered_kallisto with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2316
Currently proccessing: pipeline_filtered_salmon with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2225
Currently proccessing: pipeline_filtered_strgtieh with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 1982
Currently proccessing: pipeline_filtered_strgties with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2052
Currently proccessing: soft_filtered_htsh with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2033
Currently proccessing: soft_filtered_htss with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2100
Currently proccessing: soft_filtered_kallisto with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2274
Currently proccessing: soft_filtered_salmon with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2165
Currently proccessing: soft_filtered_strgtieh with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 1933
Currently proccessing: soft_filtered_strgties with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2023
Currently proccessing: unfiltered_htsh with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2030
Currently proccessing: unfiltered_htss with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2103
Currently proccessing: unfiltered_kallisto with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2277
Currently proccessing: unfiltered_salmon with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2168
Currently proccessing: unfiltered_strgtieh with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 1932
Currently proccessing: unfiltered_strgties with edgeR
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 The number of significant DE genes is: 2028
Currently proccessing: hard_filtered_htsh with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 Results where FDR is less than 0.01: 1970
Results where FDR is less than 0.05: 4043
Results where FDR is less than 0.1: 5169 groupAdLib - groupRestricted
Down 2066
NotSig 10258
Up 1977
Currently proccessing: hard_filtered_htss with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2067
Results where FDR is less than 0.05: 4140
Results where FDR is less than 0.1: 6661 groupAdLib - groupRestricted
Down 2092
NotSig 10161
Up 2048
Currently proccessing: hard_filtered_kallisto with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2308
Results where FDR is less than 0.05: 4315
Results where FDR is less than 0.1: 6755 groupAdLib - groupRestricted
Down 2172
NotSig 9986
Up 2143
Currently proccessing: hard_filtered_salmon with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2046
Results where FDR is less than 0.05: 4042
Results where FDR is less than 0.1: 5143 groupAdLib - groupRestricted
Down 2068
NotSig 10259
Up 1974
Currently proccessing: hard_filtered_strgtieh with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 1806
Results where FDR is less than 0.05: 3968
Results where FDR is less than 0.1: 5223 groupAdLib - groupRestricted
Down 1989
NotSig 10333
Up 1979
Currently proccessing: hard_filtered_strgties with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 1816
Results where FDR is less than 0.05: 4018
Results where FDR is less than 0.1: 5235 groupAdLib - groupRestricted
Down 2014
NotSig 10283
Up 2004
Currently proccessing: pipeline_filtered_htsh with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2081
Results where FDR is less than 0.05: 4033
Results where FDR is less than 0.1: 5116 groupAdLib - groupRestricted
Down 2066
NotSig 9945
Up 1967
Currently proccessing: pipeline_filtered_htss with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2100
Results where FDR is less than 0.05: 4150
Results where FDR is less than 0.1: 5262 groupAdLib - groupRestricted
Down 2138
NotSig 10181
Up 2012
Currently proccessing: pipeline_filtered_kallisto with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2313
Results where FDR is less than 0.05: 4320
Results where FDR is less than 0.1: 5501 groupAdLib - groupRestricted
Down 2208
NotSig 10114
Up 2112
Currently proccessing: pipeline_filtered_salmon with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2123
Results where FDR is less than 0.05: 4015
Results where FDR is less than 0.1: 5141 groupAdLib - groupRestricted
Down 2059
NotSig 9653
Up 1956
Currently proccessing: pipeline_filtered_strgtieh with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 1994
Results where FDR is less than 0.05: 4031
Results where FDR is less than 0.1: 5251 groupAdLib - groupRestricted
Down 2023
NotSig 9550
Up 2008
Currently proccessing: pipeline_filtered_strgties with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 1937
Results where FDR is less than 0.05: 4085
Results where FDR is less than 0.1: 5292 groupAdLib - groupRestricted
Down 2042
NotSig 9917
Up 2043
Currently proccessing: soft_filtered_htsh with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 1996
Results where FDR is less than 0.05: 4112
Results where FDR is less than 0.1: 7160 groupAdLib - groupRestricted
Down 2103
NotSig 12419
Up 2009
Currently proccessing: soft_filtered_htss with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2039
Results where FDR is less than 0.05: 4211
Results where FDR is less than 0.1: 7075 groupAdLib - groupRestricted
Down 2171
NotSig 12320
Up 2040
Currently proccessing: soft_filtered_kallisto with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2259
Results where FDR is less than 0.05: 4373
Results where FDR is less than 0.1: 7162 groupAdLib - groupRestricted
Down 2249
NotSig 12158
Up 2124
Currently proccessing: soft_filtered_salmon with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2069
Results where FDR is less than 0.05: 4091
Results where FDR is less than 0.1: 7264 groupAdLib - groupRestricted
Down 2095
NotSig 12440
Up 1996
Currently proccessing: soft_filtered_strgtieh with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 1823
Results where FDR is less than 0.05: 4005
Results where FDR is less than 0.1: 7831 groupAdLib - groupRestricted
Down 1990
NotSig 12526
Up 2015
Currently proccessing: soft_filtered_strgties with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 1864
Results where FDR is less than 0.05: 4099
Results where FDR is less than 0.1: 7485 groupAdLib - groupRestricted
Down 2053
NotSig 12432
Up 2046
Currently proccessing: unfiltered_htsh with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 1986
Results where FDR is less than 0.05: 4092
Results where FDR is less than 0.1: 7135 groupAdLib - groupRestricted
Down 2119
NotSig 12520
Up 1973
Currently proccessing: unfiltered_htss with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2038
Results where FDR is less than 0.05: 4211
Results where FDR is less than 0.1: 7081 groupAdLib - groupRestricted
Down 2171
NotSig 12401
Up 2040
Currently proccessing: unfiltered_kallisto with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2256
Results where FDR is less than 0.05: 4368
Results where FDR is less than 0.1: 7165 groupAdLib - groupRestricted
Down 2245
NotSig 12244
Up 2123
Currently proccessing: unfiltered_salmon with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 2065
Results where FDR is less than 0.05: 4087
Results where FDR is less than 0.1: 7266 groupAdLib - groupRestricted
Down 2091
NotSig 12525
Up 1996
Currently proccessing: unfiltered_strgtieh with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 1832
Results where FDR is less than 0.05: 4004
Results where FDR is less than 0.1: 7833 groupAdLib - groupRestricted
Down 1990
NotSig 12608
Up 2014
Currently proccessing: unfiltered_strgties with Limma-Voom
Column names: C4 C5 C2 C3 E2 E3 C6 E1 E4 E6 
Results where FDR is less than 0.01: 1869
Results where FDR is less than 0.05: 4092
Results where FDR is less than 0.1: 7485 groupAdLib - groupRestricted
Down 2051
NotSig 12520
Up 2041
It would be great to hold all plots for each data set until the end (exploratory and mean difference plots)
---
title: "DGE Analyses"
output: 
  html_notebook:
   code_folding: show
author: "Amanda D. Clark"
---

## Purpose
WORDS   
### Sources & Resources
Sources and resources are linked where applicable
## Setting Up
### Setting Up Environment
```{r}
# clear workspace
rm(list=ls(all.names=TRUE))
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

#BiocManager::install("ballgown")
#BiocManager::install("RNAseq123")
library(devtools)
library(tidyverse)
library(readr) #readr is faster, but makes tibbles...not sure how well tibbles work with programs expecting matrices. row names can't be specified with readr
library(DESeq2)
library(SummarizedExperiment)
library(edgeR)
library(limma)
library(tools)
library(RNAseq123)
library(Glimma)
library(ballgown)
library(genefilter)
library(RColorBrewer)
library(cowplot)




# directory for input files
indir <- "../R_outputs/QuantPrep_Filter"

# make a directory for output files
if (! dir.exists("../R_outputs/DGE_Analyses")) {
 dir.create("../R_outputs/DGE_Analyses")
}
outdir <- "../R_outputs/DGE_Analyses"
```


### Setting up Input Files

words

I can individual load the data for each pipeline separately, and add them to a list 
```{r}
# reading in count data
hf_htsh <- read.csv(file.path(indir, "hard_filtered_htsh.csv"), header = T, row.names = 1 ) #Hard-filtered (see Purpose) count matrices
hf_htss <- read.csv(file.path(indir,"hard_filtered_htss.csv"), header = T, row.names = 1 )
hf_kall <- read.csv(file.path(indir,"hard_filtered_kallisto.csv"), header = T, row.names = 1 )
hf_salm <- read.csv(file.path(indir,"hard_filtered_salmon.csv"), header = T, row.names = 1 )
hf_strh <- read.csv(file.path(indir,"hard_filtered_strgtieh.csv"), header = T, row.names = 1 )
hf_strs <- read.csv(file.path(indir,"hard_filtered_strgties.csv"), header = T, row.names = 1)

sf_htsh <- read.csv(file.path(indir,"soft_filtered_htsh.csv"), header = T, row.names = 1)  #Soft-filtered count matrices
sf_htss <- read.csv(file.path(indir,"soft_filtered_htss.csv"), header = T, row.names = 1)
sf_kall <- read.csv(file.path(indir,"soft_filtered_kallisto.csv"), header = T, row.names = 1)
sf_salm <- read.csv(file.path(indir,"soft_filtered_salmon.csv"), header = T, row.names = 1)
sf_strh <- read.csv(file.path(indir,"soft_filtered_strgtieh.csv"), header = T, row.names = 1)
sf_strs <- read.csv(file.path(indir,"soft_filtered_strgties.csv"), header = T, row.names = 1)

# make list of dataframes
datlist <- list(hf_htsh=hf_htsh,hf_htss=hf_htss,hf_kall=hf_kall,hf_salm=hf_salm,
                hf_strh=hf_strh,hf_strs=hf_strs,sf_htsh=sf_htsh,sf_htss=sf_htss,
                sf_kall=sf_kall,sf_salm=sf_salm,sf_strh=sf_strh,sf_strs=sf_strs)

# adding in sample metadata
samples <- read.table("../R_inputs/samples.txt", header = T) #Read in sample table
  #make new column for treatment (control vs Experiment)
samples <- samples %>% dplyr::select(SRRID, SAMPNAME) %>%  #select the sample ID and name
  mutate(Treat = ifelse(grepl("C", samples$SAMPNAME), "Restricted", #make new column for treatment (restricted vs adlib)
                            ifelse(grepl("E", samples$SAMPNAME), "AdLib", "Error")))
## make a note to change 
head(samples)

```

Or I can generate a List object with the file names and data
```{r}
# sample metadata
samples <- read.table("../R_inputs/samples.txt", header = T) #Read in sample table
  #make new column for treatment (control vs Experiment)
samples <- samples %>% dplyr::select(SRRID, SAMPNAME) %>%  #select the sample ID and name
  mutate(Treat = ifelse(grepl("C", samples$SAMPNAME), "Restricted", #make new column for treatment (restricted vs adlib)
                            ifelse(grepl("E", samples$SAMPNAME), "AdLib", "Error")))

# generate data vector
files <- list() # empty list for file paths
count_data <- vector(mode = "list", length = 2) # empty list for data vector
files <- list.files(indir, ".csv", full.names = T) # populate list from input directory

count_data <- list(f_name = c(file_path_sans_ext(basename(files))), f_content = files %>% map(read.csv, header = T, row.names =1)) # populate list with file names, paths, and content
names(count_data$f_content) <- count_data$f_name # name matrices based on file names
```



## DGE Functions 

### Library Visualization Function

words
https://multithreaded.stitchfix.com/blog/2015/10/15/multiple-hypothesis-testing/ # explains MHT well, and in depth. pull what is needed, link the rest

```{r}
run.Vis <- function(x, y) {
  y <- y[[1]][1]
  cat("Currently visualizing libraries for pipeline:", y, "\n\n") #print which file is being processed
  colnames(x) <- c(samples$SAMPNAME) #add column names
  cat("\nColumn names:", names(x), "\n\n") #print column names
  # possible issue, this object may not be the same across programs, but that shouldn't be a problem if it's just a data format. all information going into downstream programs are the same.
  dat <- DGEList(x, group = as.factor(c(samples$Treat))) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples 
  print(dat)
  
  
  # look at raw lib size in parallel
  barplot(dat$samples$lib.size*1e-6, names = 1:10, ylab = "Library size (millions)", xlab = y) #Make barplot of the library sizes
  # Saved in the object
  b.plot = recordPlot()
  dev.off()
  
  cpm <- cpm(dat)
  lcpm <- cpm(dat, log = TRUE)
  L <- mean(dat$samples$lib.size) * 1e-6
  M <- median(dat$samples$lib.size) * 1e-6
  cat("\n", "Mean and Median Library Size in Millions:", c(L, M),"\n")
  cnttab <- table(rowSums(dat$counts == 0) == 10) 
  cnttab 
 
  # MDS Plots
  col.group <- as.factor(c(samples$Treat))
  levels(col.group) <- brewer.pal(nlevels(col.group), "Set1")
  col.group <- as.character(col.group)
  mds.plot <- plotMDS(lcpm, labels = samples$SAMPNAME, col = col.group, main = paste0("Pipeline: ", y))
  # Saved in the object
  mds.plot = recordPlot()
  dev.off()
  # Density Plots to observe filtering effects (may need to reformat output to a list and then organize them after running the function to compare hard/soft filters)
  col.group <- brewer.pal(ncol(x), "Set3")
  lcpm.cutoff <- log2(10/M + 2/L)
  plot(density(lcpm[,1]), col = col.group[1], lwd = 2, ylim = c(0,0.26), las = 2, main = y)
  for (i in 2:ncol(x)){
  den <- density(lcpm[,i])
  lines(den$x, den$y, col = col.group[i], lwd = 2)
  }
  legend("topright", samples$SAMPNAME, text.col = col.group, bty = "n")
  abline(v = lcpm.cutoff, lty = 3)
  # Saved in the object
  density.res = recordPlot()
  dev.off()
 
  # Box Plots
  dat2 <- calcNormFactors(dat, method = "TMM")
  lcpm2 <- cpm(dat2, log=TRUE)
  print(head(lcpm))
  print(head(lcpm2))
  dat$samples$norm.factors
  dat2$samples$norm.factors
  par(mfrow=c(1,2)) #getting 
  boxplot(lcpm, las = 2, col = col.group, main = "Unnormalized data", ylab = "Log-CPM")
  boxplot(lcpm2, las = 2, col = col.group, main = "Normalized data", ylab = "Log-CPM")
  box.res = recordPlot()
  dev.off()
  
  pdf(file = paste0(outdir,"/",y,"_dataExploration.pdf"))
  print(b.plot)
  print(mds.plot)
  print(density.res)
  print(box.res)
  dev.off()
}
```

### DESeq2 DGE Function

words

```{r}
run.DESeq <- function(x, y) {
  y <- y[[1]][1]
  cat("Currently proccessing:", y, "with DESeq \n") #print which file is being processed
  colnames(x) <- c(samples$SAMPNAME) #add column names
  cat("\nColumn names:", names(x), "\n\n") #print column names 
  
  dat <- DESeqDataSetFromMatrix(countData = x, colData = samples, 
                                design = ~Treat) #create DESeq object, merging counts, metadata, and specifies the predictor variable for gene counts
  print(dat)
  cat("\nResults Below: \n")
  mod <- DESeq(dat, minReplicatesForReplace = Inf) #running the DESeq function. minReplicatesForReplace=Inf prevents replacement of outlier counts
  res <- results(mod, independentFiltering = FALSE,cooksCutoff = FALSE, contrast = c("Treat", "Restricted", "AdLib"),
                 pAdjustMethod = "fdr") #store results table. skipping outlier adjustments and additional low count filtering. using a false discovery rate p-value adjustment
  print(head(res))
  print(summary(res))
  
  # make data frame output, reorder, and filter
  reslist <- list( GeneID = res@rownames, meanExpr = res@listData$baseMean, logFC = res@listData$log2FoldChange, 
                   pval = res@listData$pvalue, adj.pval = res@listData$padj)
  resdf <- as.data.frame(do.call(cbind, reslist)) %>% mutate(meanExpr = as.numeric(meanExpr), pval = as.numeric(pval), 
                                                             adj.pval = as.numeric(adj.pval))
  
  resOrdered <- resdf[order(as.numeric(resdf$adj.pval)),] #results reordered by the adjusted pvalue
  resSig <- subset(resOrdered, as.numeric(adj.pval) < 0.05)
  print(head (resSig))
  print(summary(resSig))
  
  out <- resSig
  
  write.csv(as.data.frame(out),file=paste0(outdir,"/",y,"_DESeq2.csv")) #write results to a new csv
  
  pdf(file = paste0(outdir,"/",y,"_DESeq.pdf"))
  DESeq2::plotMA(res)
  dev.off()
  
}
```


### edgeR DGE Function

words

```{r}
run.EdgeR <- function(x, y) {
  y <- y[[1]][1]
  cat("Currently proccessing:", y, "with edgeR \n") #print which file is being processed
  colnames(x) <- c(samples$SAMPNAME) #add column names
  cat("\nColumn names:", names(x), "\n\n") #print column names 
  
  
  dat <- DGEList(x, group = samples$Treat) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples 
  
  # est common & tagwise dispersion
  mod <- estimateCommonDisp(dat)
  mod <- estimateTagwiseDisp(mod)
  
  # perform exact test btwn caloric restriction & ad lib groups, store as 'res'
  modTest <- exactTest(mod)
  res <- topTags(modTest, n = nrow(modTest$table))
  
  # extract significant differentially expressed genes, sort, & write to csv
  resOrdered <- res$table[order(res$table$logFC),]
  resSig <- resOrdered[resOrdered$FDR<0.05,]
  print(head(resOrdered))
  
  out <- resSig %>% dplyr::select(logFC, logCPM, PValue, FDR) %>% dplyr::rename(meanExpr = logCPM, pval = PValue, adj.pval = FDR)
  write.csv(as.data.frame(out),file = paste0(outdir,"/",y,"_edgeR.csv")) #write results to a new csv

  cat("The number of significant DE genes is: ", nrow(resSig),"\n\n")
  
  pdf(file = paste0(outdir,"/",y,"_edgeR.pdf"))
  edgeR::plotMD.DGEExact(modTest)
  #plotMD(res, column = 5, main = paste(colnames(res)[1],y,sep = "_"), xlim = c(-0.1,20))
  dev.off()
  
  
}
```

### LimmaVoom DGE Function

words
Summarize & simplify:
"What is voom doing?
Counts are transformed to log2 counts per million reads (CPM), where “per million reads” is defined based on the normalization factors we calculated earlier
A linear model is fitted to the log2 CPM for each gene, and the residuals are calculated
A smoothed curve is fitted to the sqrt(residual standard deviation) by average expression (see red line in plot above)
The smoothed curve is used to obtain weights for each gene and sample that are passed into limma along with the log2 CPMs.
More details at https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29"



```{r}
run.LimVoo <- function(x,y) {
  y <- y[[1]][1]
  cat("Currently proccessing:", y ,"with Limma-Voom \n") #print which file is being processed by which function as a sanity check
  colnames(x) <- c(samples$SAMPNAME) #add column names to the data object
  cat("\nColumn names:", names(x), "\n\n") #print column names as a sanity check for order
  
  Treat <- c(samples$Treat)
  group=Treat
  dat <- DGEList(x, group = Treat) #create DGEList object, merging counts, metadata, and specifies the grouping variable for samples 
  #print(class(dat))
  
  # Normalization (based on the plots, is this necessary?)
  dat <- calcNormFactors(dat, method = "TMM")
  dat$samples$norm.factors
  dat
  
  mod <- model.matrix(~0 + group)
  mod
  
  varMod <- voom(dat, mod, plot = T) # Would be nice to stop and compare hard/soft filtering again here
  
  modFit <- lmFit(varMod, mod)
  #print(head(coef(modFit)))
  
  contr <- makeContrasts(groupAdLib - groupRestricted, levels = colnames(coef(modFit)))
  #print(head(contr))
  
  fitContr <- contrasts.fit(modFit, contr)
  fitContr <- eBayes(fitContr)
  
  res <- topTable(fitContr, sort.by = "P", n = Inf)
  print(head(res, 8)) 
  cat("Results where FDR is less than 0.01: ", length(which(res$adj.P.Val < 0.01)), "\n")
  cat("Results where FDR is less than 0.05: ", length(which(res$adj.P.Val < 0.05)), "\n")
  cat("Results where FDR is less than 0.1: ", length(which(res$adj.P.Val < 0.1)), "\n")
  
  out <- res %>% dplyr::select(logFC, AveExpr, P.Value, adj.P.Val) %>% dplyr::rename(meanExpr = AveExpr, pval = P.Value, adj.pval = adj.P.Val)
  print(head(out))
  
  etRes <- decideTests(fitContr)
  print(summary(etRes))
  
  write.csv(as.data.frame(out),file = paste0(outdir,"/",y,"_LimmaVoom.csv")) #write results to a new csv
  
  pdf(file = paste0(outdir,"/",y,"_LimmaVoom.pdf"))
  plotMD(fitContr, column = 1, status = etRes[,1], main = paste(colnames(fitContr)[1],y,sep = "_"), xlim = c(-0.1,20))
  varMod <- voom(dat, mod, plot = T)
  dev.off()
  
}
```

## Ballgown DGE 
words
Summarize & simplify:
There are many ballgown specific input files that make it difficult to use the previously filtered data with these programs. Only things processed with stringtie with the for ballgown output are readily formatted for this program. These will not be run within a function.?
Some resources: https://rnabio.org/module-03-expression/0003/04/01/DE_Visualization/ 
https://rstudio-pubs-static.s3.amazonaws.com/289617_cb95459057764fdfb4c42b53c69c6d3f.html
https://davetang.org/muse/2017/10/25/getting-started-hisat-stringtie-ballgown/
```{r}

# We loaded our "phenotype" data in the beginning, so we don't need to repeat this step. 
# create a ballgown object for the star and hisat2 outputs; stringtie and ballgown are complementary programs

bg_star <- ballgown(dataDir = "../R_inputs/ballgown_star/", samplePattern = "SRR", pData = samples)
bg_hisat <- ballgown(dataDir = "../R_inputs/ballgown_hisat/", samplePattern = "SRR", pData = samples)

# check out the objects
class(bg_star)
class(bg_hisat)

bg_star
bg_hisat

# filtering, following previous logic for pipeline specific

bg_star_f1 <- ballgown::subset(bg_star, 
                               "rowSums(gexpr(bg_star)==0) <= 5", 
                               genomesubset=TRUE) # first filter, remove rows with 6 or more 0s
bg_star_f1
bg_star_fltrd <- ballgown::subset(bg_star_f1, 
                                  "rowSums(gexpr(bg_star_f1)) >= 21") # second filter, remove rows that sum to less than 21
bg_star_fltrd


bg_hisat_f1 <- ballgown::subset(bg_hisat, 
                                "rowSums(gexpr(bg_hisat)==0) <= 5", 
                                genomesubset=TRUE) # first filter, remove rows with 6 or more 0s
bg_hisat_f1
bg_hisat_fltrd <- ballgown::subset(bg_hisat_f1, 
                                   "rowSums(gexpr(bg_hisat_f1)) >= 21") # second filter, remove rows that sum to less than 21
bg_hisat_fltrd

# run dge analysis and output data to file (should filter by qvalue? -- what did I filter by with other tables?)
bg_star_genes <- stattest(bg_star_fltrd,
                          feature="gene",
                          covariate="Treat",
                          getFC=TRUE, meas="FPKM")
dim(bg_star_genes)
table(bg_star_genes$qval<0.05)


bg_hisat_genes <- stattest(bg_hisat_fltrd,
                          feature="gene",
                          covariate="Treat",
                          getFC=TRUE, meas="FPKM")

dim(bg_hisat_genes)
table(bg_hisat_genes$qval<0.05)

# output results
# extract significant differentially expressed genes, sort, & write to csv

bg_hisat_genes[,"de"] <- log2(bg_hisat_genes[,"fc"])
sigpi = which(bg_hisat_genes[,"pval"]<0.05)
sigp = bg_hisat_genes[sigpi,]
sigde = which(abs(sigp[,"de"]) >= 2)
sig_tn_de = sigp[sigde,]
o = order(sig_tn_de[,"qval"], -abs(sig_tn_de[,"de"]), decreasing=FALSE)
output = sig_tn_de[o,c("id","fc","pval","qval","de")]
write.csv(as.data.frame(output),file = paste0(outdir,"/","hisat_Ballgown.csv")) #write results to a new csv

bg_star_genes[,"de"] <- log2(bg_star_genes[,"fc"])
sigpi = which(bg_hisat_genes[,"pval"]<0.05)
sigp = bg_hisat_genes[sigpi,]
sigde = which(abs(sigp[,"de"]) >= 2)
sig_tn_de = sigp[sigde,]
o = order(sig_tn_de[,"qval"], -abs(sig_tn_de[,"de"]), decreasing=FALSE)
output <- sig_tn_de[o,c("id","fc","pval","qval","de")]
write.csv(as.data.frame(output),file = paste0(outdir,"/","star_Ballgown.csv")) #write results to a new csv

# visualize results

bg_star_genes$mean <- rowMeans(texpr(bg_star_fltrd))
bg_star_plot <- ggplot(bg_star_genes, aes(log2(mean), log2(fc), colour = qval<0.05)) +
  scale_color_manual(values=c("#999999", "#FF0000")) +
  geom_point() +
  geom_hline(yintercept=0)

bg_hisat_genes$mean <- rowMeans(texpr(bg_hisat_fltrd))
bg_hisat_plot <- ggplot(bg_hisat_genes, aes(log2(mean), log2(fc), colour = qval<0.05)) +
  scale_color_manual(values=c("#999999", "#FF0000")) +
  geom_point() +
  geom_hline(yintercept=0)

bg_star_plot
bg_hisat_plot

```




## Apply DGE functions

I am applying each function in loop here
```{r}

cnt <- 1
for (i in datlist){
run.Vis(i, names(datlist)[cnt])
cnt <- cnt +1
}

cnt <- 1
for (i in datlist){
run.DESeq(i, names(datlist)[cnt])
cnt <- cnt +1
}

cnt <- 1
for (i in datlist){
run.EdgeR(i, names(datlist)[cnt])
cnt <- cnt +1
}

cnt <- 1
for (i in datlist){
run.LimVoo(i, names(datlist)[cnt])
cnt <- cnt +1
}
```

I can also map or mapply over the data set, looping over each DEseq function
```{r}
# List of functions needed to run on count matrices
funct <- c( "run.Vis","run.DESeq", "run.EdgeR", "run.LimVoo")

# Apply each function to each count data set in List object
for (func in funct) {
mapply(func, count_data$f_content, count_data$f_name)
}

```
It would be great to hold all plots for each data set until the end (exploratory and mean difference plots)

